This document is an excerpt from the EUR-Lex website
Document 52012SC0041
COMMISSION STAFF WORKING DOCUMENT Impact Assessment on the role of land use, land use change and forestry (LULUCF) in the EU's climate change commitments Accompanying the document Proposal for a DECISION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on accounting rules and action plans on greenhouse gas emissions and removals resulting from activities related to land use, land use change and forestry
COMMISSION STAFF WORKING DOCUMENT Impact Assessment on the role of land use, land use change and forestry (LULUCF) in the EU's climate change commitments Accompanying the document Proposal for a DECISION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on accounting rules and action plans on greenhouse gas emissions and removals resulting from activities related to land use, land use change and forestry
COMMISSION STAFF WORKING DOCUMENT Impact Assessment on the role of land use, land use change and forestry (LULUCF) in the EU's climate change commitments Accompanying the document Proposal for a DECISION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on accounting rules and action plans on greenhouse gas emissions and removals resulting from activities related to land use, land use change and forestry
/* SWD/2012/0041 - COD/2012/0042 */
COMMISSION STAFF WORKING DOCUMENT Impact Assessment on the role of land use, land use change and forestry (LULUCF) in the EU's climate change commitments Accompanying the document Proposal for a DECISION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on accounting rules and action plans on greenhouse gas emissions and removals resulting from activities related to land use, land use change and forestry /* SWD/2012/0041 - COD/2012/0042 */
TABLE OF CONTENTS 1........... Procedural issues and consultation of interested parties.................................................. 10 1.1........ Introduction.................................................................................................................. 10 1.2........ Organisation and timing................................................................................................. 12 1.3........ Consultation and expertise............................................................................................ 12 1.4........ Opinion of the Impact Assessment Board...................................................................... 13 2........... Problem definition......................................................................................................... 13 2.1........ What is the issue or problem that may require action?.................................................... 13 2.1.1..... Enhancing the environmental integrity of the EU's climate change
commitments............... 13 2.1.2..... Strengthening synergies with wider policy objectives...................................................... 13 2.1.3..... Preparing the ground for more ambitious targets............................................................ 15 2.2........ What issues need to be addressed to include LULUCF in accounting and
what is the current state of play? 15 2.2.1..... Addressing the non-permanence of emissions and removals and the
influence of natural effects on emissions and removals....................................................................................................................... 15 2.2.2..... Ensuring robust monitoring and reporting....................................................................... 16 2.2.2.1.. Completeness of estimates............................................................................................ 17 2.2.2.2.. Accuracy of estimates................................................................................................... 17 2.2.2.3.. Time consistency and comparability of estimates............................................................ 18 2.2.3..... Ensuring robust accounting rules.................................................................................... 19 2.2.4..... Defining the policy context for the inclusion of LULUCF in the EU's
climate change commitment 20 2.3........ Who is affected?........................................................................................................... 21 2.4........ How would the problem evolve, all things equal?........................................................... 22 2.5........ Does the EU have the right to act and is EU added-value evident?................................. 24 3........... Objectives.................................................................................................................... 24 3.1........ What are the general and more specific / operational objectives?.................................... 24 4........... Policy options............................................................................................................... 25 4.1........ What are the possible options for tackling the problem and meeting
the operational objectives? 25 4.2........ Options for the policy context in which to include LULUCF in the
EU's GHG emission reduction commitments.................................................................................................................................... 26 4.2.1..... Option 1 – No EU action.............................................................................................. 27 4.2.2..... Option 2 – Include LULUCF as a separate policy framework....................................... 27 4.2.3..... Option 3 – Include LULUCF in the Effort Sharing Decision........................................... 28 4.3........ Options for improving monitoring and reporting............................................................. 29 4.4........ Options for accounting.................................................................................................. 30 4.4.1.1.. Accounting option (a): Small changes to the current Kyoto Protocol
rules....................... 32 4.4.1.2.. Accounting option (b): Likely outcome in the UNFCCC negotiations............................. 33 4.4.1.3.. Accounting option (c): UNFCCC+............................................................................... 34 4.5........ Which options have been discarded at an early stage and why?...................................... 35 5........... Analysis of impacts....................................................................................................... 35 5.1........ Environmental implications............................................................................................ 35 5.1.1..... Impact of the different options on GHG emissions.......................................................... 35 5.1.1.1.. Option 2.I – Separate framework for LULUCF (without mitigation
targets)................... 35 5.1.1.2.. Option 2.II – Separate framework for LULUCF (with mitigation
targets)....................... 38 5.1.1.3.. Option 3 – Inclusion of LULUCF in the ESD................................................................ 38 5.1.2..... Other environmental impacts......................................................................................... 39 5.1.3..... Possible contributions to an EU target........................................................................... 40 5.2........ Economic implications................................................................................................... 42 5.2.1..... Direct abatement costs.................................................................................................. 42 5.2.1.1.. Option 2.II – Separate framework with mitigation targets............................................... 42 5.2.1.2.. Option 3 – Include LULUCF in the ESD...................................................................... 42 5.2.2..... Costs for the various sectors......................................................................................... 43 5.2.2.1.. Agriculture.................................................................................................................... 43 5.2.2.2.. Forestry....................................................................................................................... 44 5.2.2.3.. Impacts by owner structure........................................................................................... 46 5.2.3..... Monitoring and reporting costs...................................................................................... 46 5.2.3.1.. Administrative burden of changing the scope and methods of
accounting........................ 46 5.2.3.2.. Implications of improved monitoring and reporting......................................................... 47 5.3........ Social implications........................................................................................................ 49 5.3.1..... Costs by Member States.............................................................................................. 49 5.3.1.1.. Option 2.II – Separate framework with mitigation targets............................................... 49 5.3.1.2.. Option 3 – LULUCF included in the ESD..................................................................... 50 5.3.2..... Employment................................................................................................................. 50 6........... Comparing the options.................................................................................................. 51 6.1.1..... Choosing the right policy context................................................................................... 51 6.1.2..... Improving monitoring and reporting............................................................................... 53 6.2........ Concluding comments................................................................................................... 53 7........... Monitoring and evaluation............................................................................................. 54 8........... ANNEXES.................................................................................................................. 55 8.1........ Annex I – Monitoring, reporting and verification............................................................ 55 8.1.1..... Introduction.................................................................................................................. 55 8.1.2..... Completeness............................................................................................................... 55 8.1.2.1.. Convention reporting.................................................................................................... 55 8.1.2.2.. Kyoto Protocol reporting.............................................................................................. 62 8.1.2.3.. Concluding comments................................................................................................... 63 8.1.3..... Accuracy...................................................................................................................... 64 8.1.3.1.. Underlying methods used to estimate activity data.......................................................... 65 8.1.4..... Underlying methods used to estimate emission factors.................................................... 68 8.1.5..... Uncertainties................................................................................................................. 74 8.1.6..... Recalculations............................................................................................................... 76 8.1.7..... Time-series consistency and comparability between MSs............................................... 77 8.1.7.1.. Definitions.................................................................................................................... 77 8.1.8..... Verification activities..................................................................................................... 79 8.1.9..... Costs of improving monitoring....................................................................................... 79 8.1.9.1.. Step 1 – Achieving completeness in the reporting of categories and
pools at a minimum level of tier 1 80 8.1.9.2.. Step 2 – Increasing the accuracy of the reported key categories and
pools to a minimum of tier 2 81 8.1.9.3.. 3 – Harmonisation........................................................................................................ 82 8.1.9.4.. Results......................................................................................................................... 83 8.1.10... Appendix 1 – Overview of problems emerged during 2010 review and
answers by MS. 84 8.2........ Annex II – Impacts on accounting of threshold values (natural
disturbances) and caps (forest management) 87 8.2.1..... Implications of different triggers for the application of provisions
for large natural disturbances 87 8.2.2..... Caps on credits and debits in forest management and their
implications........................... 88 8.3........ Annex III – Methodology (models and calibration)........................................................ 90 8.3.1..... Description of the models, their linkages and their use.................................................... 90 8.3.2..... Description of uncertainties in the modelling results......................................................... 93 8.3.3..... Description of the calibration method used..................................................................... 94 8.4........ Annex IV – Model results: projected emissions / removals and
abatement costs............. 96 8.4.1..... Projected emissions and removals (Reference scenario)................................................. 96 8.4.1.1.. Models average............................................................................................................ 97 8.4.1.2.. G4M and EUFASOM.................................................................................................. 97 8.4.1.3.. EFISCEN and EUFASOM.......................................................................................... 97 8.4.1.4.. Accounting results for individual MSs and the EU-27(models average)........................... 98 8.4.2..... Distribution of costs.................................................................................................... 101 8.5........ Annex V – Discarded options..................................................................................... 102 8.5.1..... Discarded policy options............................................................................................. 102 8.5.1.1.. Splitting LULUCF according to activities for the inclusion in
different policy frameworks 103 8.5.2..... Discarded accounting options...................................................................................... 103 9........... Bibliography............................................................................................................... 105 COMMISSION STAFF WORKING DOCUMENT Impact Assessment on the role of land use,
land use change and forestry (LULUCF) in the EU's climate change commitments Accompanying the document Proposal for a DECISION OF THE
EUROPEAN PARLIAMENT AND OF THE COUNCIL on accounting rules and action
plans on greenhouse gas emissions and removals resulting from activities related
to land use, land use change and forestry List of acronyms AD || Activity Data A || Afforestation AR || Afforestation / Reforestation ARD || Afforestation, Reforestation and Deforestation BAU || Business as Usual CAP || Common Agricultural Policy CL || Cropland CM || Cropland management COP || Conference of the Parties COST || European Cooperation in Science and Technology D || Deforestation E || Emissions EF || Emission Factor EFISCEN || European Forest Information Scenario ESD || Effort Sharing Decision EU ETS || EU Emissions Trading System FAO || Food and Agriculture Organization of the United Nations. FL || Forest land FM || Forest Management G4M || Global Forest Model GDP || Gross Domestic Product GHG || Greenhouse gas GL || Grassland GM || Grazing land management GPG || Good Practice Guidance HWP || Harvested Wood Products IPCC || Inter-governmental Panel on Climate Change JRC || Joint Research Centre of the European Commission KP || Kyoto Protocol LULUCF || Land Use, Land Use Change and Forestry MRV || Monitoring, Reporting and Verification MS(s) || Member State(s) NFI || National Forest Inventories NGOs || Non-Governmental Organizations NIRs || National Inventory Reports R || Removals REDD || Reducing Emissions from Deforestation and forest Degradation RES-D || Directive on the promotion of the use of energy from renewable sources SMEs || Small- and Medium sized Enterprises RV || Revegetation TFEU || Treaty on the Functioning of the European Union UNFCCC || United Nations Framework Convention on Climate Change List of definitions[1] Accuracy || Accuracy is a relative measure of the exactness of an emission or removal estimate. Estimates should be accurate in the sense that they are systematically neither over nor under true emissions or removals, so far as can be judged, and that uncertainties are reduced so far as is practicable. Appropriate methodologies conforming to guidance on good practices should be used to promote accuracy in inventories. Activity data || Data on the magnitude of human activity resulting in emissions or removals taking place during a given period of time. In the LULUCF sector, data on land areas, management systems, lime and fertilizer use are examples of activity data. Afforestation || The direct human-induced conversion of land that has not been forested for a period of at least 50 years to forested land through planting, seeding and/or the human-induced promotion of natural seed sources. Biomass || Organic material both above ground and below ground, and both living and dead, e.g., trees, crops, grasses, tree litter, roots etc. Biomass includes the pool definition for above - and below - ground biomass. Carbon pool* || The whole or part of a geochemical feature or system within the territory of a Member State within which carbon, any precursor to a greenhouse gas containing carbon or any greenhouse gas containing carbon is stored. Carbon stock* || The quantity of the element carbon stored in a carbon pool. Comparability || Comparability means that estimates of emissions and removals reported by Parties in inventories should be comparable among Parties. For this purpose, Parties should use the methodologies and formats agreed by the Conference of the Parties (COP) for estimating and reporting inventories. Completeness || Completeness means that an inventory covers all sources and sinks for the full geographic coverage, as well as all gases included in the IPCC Guidelines in addition to other existing relevant source/sink categories which are specific to individual Parties (and therefore may not be included in the IPCC Guidelines). Consistency || Consistency means that an inventory should be internally consistent in all its elements over a period of years. An inventory is consistent if the same methodologies are used for the base year and all subsequent years and if consistent data sets are used to estimate emissions or removals from sources or sinks. Under certain circumstances referred to in paragraphs 10 and 11 of FCCC/SBSTA/1999/6/Add.1, an inventory using different methodologies for different years can be considered to be consistent if it has been recalculated in a transparent manner taking into account any good practices. Cropland || This category includes arable and tillage land, and agro-forestry systems where vegetation falls below the threshold used for the forest land category, consistent with the selection of national definitions. Cropland management* || Any activity resulting from a system of practices applicable to land on which agricultural crops are grown and on land that is set aside or temporarily not being used for crop production Deforestation || The direct human-induced conversion of forested land to non-forested land. Disturbances* || Events including wildfires, insect and disease infestations, extreme weather events and geological disturbances, but not harvesting. Emission factor || A coefficient that relates the activity data to the amount of chemical compound which is the source of later emissions. Emission factors are often based on a sample of measurement data, averaged to develop a representative rate of emission for a given activity level under a given set of operating conditions. Forest || An area of land of at least 0.05 hectare with tree crown cover or equivalent stocking level of at least 10 per cent, covered with trees with the potential to reach a minimum height of at least 2 metres at maturity at their place of growth, including groups of growing young natural trees and all plantations which have yet to reach a tree crown cover or equivalent stocking of at least 10 per cent or tree height of at least 2 metres, and areas normally forming part of the forest area on which there are temporarily no trees as a result of human intervention, such as harvesting, or of natural causes, but which are expected to revert to forest Forest land || The land that meets the definition of forest. This category includes all land with woody vegetation consistent with thresholds used to define forest land in the national GHG inventory, sub-divided at the national level into managed and unmanaged and also by ecosystem type as specified in the IPCC Guidelines.6 It also includes systems with vegetation that currently falls below, but is expected to exceed, the threshold of the forest land category. Forest management* || Any activity resulting from a system applicable to a forest and aimed at improving any ecological, economic or social function of the forest Grassland || This category includes rangelands and pasture land that is not considered as cropland. It also includes systems with vegetation that fall below the threshold used in the forest land category and is not expected to exceed, without human intervention, the thresholds used in the forest land category. This category also includes all grassland from wild lands to recreational areas as well as agricultural and silvo-pastural systems, subdivided into managed and unmanaged, consistent with national definitions. Grazing land management* || Any activity resulting from a system applicable to land used for livestock production and aimed at controlling or influencing the quantity and type of vegetation and livestock produced. Key category || A category that is prioritised within the national inventory system because its estimate has a significant influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both. Reforestation || The direct human-induced conversion of non-forested land to forested land through planting, seeding and/or the human-induced promotion of natural seed sources, on land that was forested but that has been converted to non-forested land. For the first commitment period, reforestation activities will be limited to reforestation occurring on those lands that did not contain forest on 31 December 1989. Removals* || Used, for the purposes of this report, synonymously with "sink". Revegetation* || The direct, human-induced activity intended to increase the carbon stock of any site that covers a minimum area of 0.05 hectares, through the establishment of vegetation, where that activity does not constitute afforestation or reforestation Sequestration || The process of increasing the carbon content of a carbon pool other than the atmosphere. It is preferred to use the term “sink”. Sink* || The rate of build-up of CO2 in the atmosphere can be reduced by taking advantage of the fact that carbon can accumulate in vegetation and soils in terrestrial ecosystems. Any process, activity or mechanism which removes a greenhouse gas from the atmosphere is referred to as a "sink." Denoted in accounting and reporting with the negative (-) sign. Transparency || Transparency means that the assumptions and methodologies used for an inventory should be clearly explained to facilitate replication and assessment of the inventory by users of the reported information. The transparency of inventories is fundamental to the success of the process for the communication and consideration of information. Wetland* || Any activity resulting from a system for draining and rewetting land that covers a minimum area of 1 hectare and on which organic soil is present, provided the activity does not constitute any other activity referred to in Article 3(1), and where draining is the direct human-induced lowering of the soil water table, and rewetting is the direct human-induced partial or total reversal of drainage.This category can be subdivided into managed and unmanaged according to national definitions. It includes reservoirs as a managed sub-division and natural rivers and lakes as unmanaged sub-divisions.
1.
Procedural issues and consultation of interested
parties
1.1.
Introduction
In order to avoid dangerous anthropogenic
interference with the climate system, the overall global annual mean surface
temperature increase should not exceed two degrees Celsius above pre-industrial
levels. This ambition was recognised in the Cancún Agreements[2] and requires global greenhouse
gas (GHG) emissions to be cut by at least 50% below 1990 levels by 2050, or by
80-95% in developed countries (IPCC, 2007), and has been embraced by the
European Council as a long-term goal.[3]
The European Parliament similarly endorsed the a long-term reduction target of
at least 80% by 2050 for the EU and other developed countries.[4] As a step towards the necessary
long-term efforts, the Union has committed to reduce its GHG emissions to 20%
below 1990 levels by 2020, or to 30% if conditions are right. The commitment
forms part of one of the European Union's five headline targets in the Europe
2020 Strategy for smart, sustainable and inclusive growth.[5] Land use, land use change and forestry
(LULUCF) is not yet part of the EU's GHG emission reduction target for 2020.
However, the European Council and Parliament requested the Commission to assess
the possibility to include LULUCF in the 2020 target and make a legislative
proposal, as appropriate. The timing of this work was made conditional on the
outcome of the negotiations of an international agreement on climate change. In
the absence of such an agreement at the end of 2010, the Commission is required
to report on the results of its assessment in 2011 with the aim of the proposed
act entering into force from 2013 onwards.[6]
Whilst substantial progress was made at the Conference of Parties to the United
Nations Framework Convention on Climate Change (UNFCCC) in Cancún in December
last year, no comprehensive agreement was reached. In fulfilment with the requirements
and for the reasons provided in Section 2.1, this impact assessment accompanies
a Communication and legislative proposal on accounting for LULUCF (agenda
planning number 2011/CLIMA/008). Emissions of greenhouse gases in the EU
mainly come from energy production and other man-made sources, see Figure 1.
But, countering some of the emissions, carbon is absorbed (removed) from the
atmosphere through photosynthesis and stored in vegetation and soils. Different
land uses and management practices in forestry and agriculture can limit
emissions of carbon and enhance removals from the atmosphere, see Box 1. Carbon removed from land can also stay bound in harvested wood
products for a long period of time without being re-emitted to the atmosphere
and the recycling of wood and production of long-lived products can therefore
contribute to mitigation efforts. These practices and uses are covered by the land use, land
use change and forestry (LULUCF) sector.[7]
In 2009, LULUCF removed an amount of carbon from the atmosphere equal to about
9% of the EU's total greenhouse gas emissions in other sectors. Figure 1. Emissions
and removals per sector in the EU-27 (2009) Note: Negative numbers denote net removals and positive
numbers net emissions. Source: EEA (2011) In addition, land
resources can contribute to mitigation efforts in other sectors through the
substitution of fossil fuels in energy production and greenhouse gas intensive
materials such as steel and cement with biomass (see
e.g. Sathre and O'Connor, 2010), see Box 1. Box 1. Possible
contribution of land use, land use change and forestry to climate change
mitigation Agriculture contributes in a number
of ways. Measures include agronomic practices such as using improved crop
varieties, extending crop rotations (a shift to perennial crops) and avoiding
or reducing the use of unplanted fallow through green cover; agro-forestry
practices which provide higher carbon stocks through the tending of livestock
or growing of food crops on land that also grows trees for timber, energy or
other wood products; adjustments of the intensity and timing of grazing on
lands can influence the growth, carbon allocation and flora of grasslands and
thereby the removals and storage of carbon in soils; enhanced productivity of croplands
and grasslands through returning or leaving organic materials (farmyard manure,
straw, crop residues) on the land; improved management of organic soils through
avoiding the drainage of these soils or re-establishing a high water table on
peat lands; and restoration of degraded lands. Similarly, there are many
mitigation opportunities in forestry. They include the conversion of
non-forest land to forest (afforestation); avoiding conversion of forest land
to other types of land (deforestation); productiivity increases, conservation
of carbon in existing forests e.g.through optimized tending and thinning,
continuous crown harvesting (selective logging), longer rotation periods of
trees, avoidance of clearfelling; conversion to wilderness forests and more
widespread use of prevention measures to limit the impacts of disturbances such
as fires, pests and storms. In addition to the
opportunities directly linked to forestry and agriculture, there are potential
mitigation benefits in the industry and energy sectors if agricultural
land and forests are managed for production of timber and energy. In this
respect enhanced production in existing forests through adjusting rotations
closer to the productive maximum, more production from low-production forests,
increasing the harvest of timber offcuts and branchwood and changing species
composition and growth rate are equally important. While carbon is stored in
vegetation and soils, it can also be stored for several decades in products,
and industry can make an important contribution to mitigation through
increasing the recycling of wood and / or production of pulp, paper and wood
products. The bio-based industry can make use of crops grown for material
substitution (e.g. hemp and grass for insulation instead of glass fibre, straw
used for furniture production, biodegradable cutlery made from maize starch,
car door panels made from flax or sisal plants, hemp plastic) or energy (e.g.
biomass for energy instead of fossil fuels). The measures in the
different sectors have different advantages and limitations. An important
consideration is that the positive impacts of measures that increase carbon
stocks in vegetation and soils eventually saturate and are potentially
impermanent. Equally, measures that avoid greenhouse gas emissions through the
use of wood or other biomass instead of other materials may have negative
impacts on carbon in vegetation in soil. It is therefore important to
substitute materials and energy sources that have relatively high greenhouse
gas emissions during their lifecycle. See Matthews et al. (2011) for more
details on the different measures and their potentials.
1.2.
Organisation and timing
For the preparation of this initiative,
close inter-service consultation was ensured through the establishment of an
inter-service group and impact assessment steering group which oversaw and
followed the impact assessment work. DG Environment, DG Agriculture and Rural
Development, the Joint Research Centre (JRC), DG Research and Innovation, DG
Energy, DG Economic and Financial Affairs, DG Enterprise and Industry, DG Legal
Services and the Secretariat General were directly involved. The work draws on input from two contracts;
one which involved investigating different options for the inclusion of the
sector in the EU's reduction commitment (Matthews et al. 2011; Watterson et al.
2011) and one which involved modelling the projected emissions and removals
from the sector as well as the impacts on the sector and Member States (MSs) of
different options (Böttcher et al., 2011). The work also builds on input from
the JRC on modelling and on monitoring and reporting (JRC 2011a and 2011b, see
Annexes II and IV to this report).
1.3.
Consultation and expertise
A number of consultative initiatives were
undertaken in support of the work: · An expert group on climate policy for LULUCF was
established in 2010 under the European Climate Change Programme . The group involved
a wide range of stakeholders: environmental NGOs, trade associations, experts
from public administrations and researchers. The objective was to define and
provide input on critical issues related to the inclusion of the LULUCF sector
in the EU's climate change mitigation efforts, i.e. a scoping and steering
exercise. The summary report is available on the Commission's website
http://ec.europa.eu/clima/events/0029/index_en.htm · In addition, an online public consultation was carried out in
2010 to collect views on the opportunities and challenges related to the
inclusion of the sector in the EU's commitments. A total of 153 responses were
received, representing the views of private companies, business and industry
organisations, individuals and private land owners, NGOs, academia and research
and public authorities. The same questions were subsequently used in a separate
consultation with MSs and received 14 responses. The results of
both consultations have been
published in reports (Entec, 2011a and 2011b)
http://ec.europa.eu/clima/events/0029/index_en.htm · The Commission also held a stakeholder meeting on 28 January
2011 in Brussels. Around 75 participants representing MSs, trade associations,
environmental NGOs and research institutes took part in the discussions. The
proceedings are available on the Commission's website.
http://ec.europa.eu/clima/events/0029/index_en.htm The key findings of the consultation process are presented in boxes at the beginning of the relevant sections of this report.
1.4.
Opinion of the Impact Assessment Board
The impact assessment (IA) was discussed at
a meeting of the IA Board (IAB) on 4 May 2011. Following the IAB's
recommendations, the report now clearly states that the option (2.II) which
incorporates mitigation targets for LULUCF is included for illustrational
purposes and to initiate further discussions. This impact assessment considers
accounting for LULUCF in the context of the 20% GHG emission reduction
commitment and not in the context of a step-up of the EU's overall efforts, as
discussed in Sections 4 and 6, and there are uncertainties associated with the
modelling and inventories which affect the potential to set targets at this
stage, see Annex III. A commentary on the model-based predicted impacts and
references to more information is provided in Annexes III and IV. The report
also provides a description of the practical and political context in which the
accounting for LULUCF is assessed throughout the report, in particular in
Sections 2.1 and 6. Finally, references to the results of the various
stakeholder consultations have been integrated in Sections 1 and 4.
2.
Problem definition
2.1.
What is the issue or
problem that may require action?
The purpose of this
report is to assess how LULUCF may be addressed in relation to the EU's GHG
emission reduction commitments. Accounting for the sector would enhance the environmental
integrity of the commitments, strengthen the overall coherence of
climate policy and improving the economic efficiency when taking on
more ambitious commitments.
2.1.1.
Enhancing the environmental integrity of the
EU's climate change commitments
The current partial accounting risks hiding
real emissions and negative trends in emissions and removals. The IPCC
Good Practice Guidance (GPG) for estimating GHG inventories (1996; 2003, see p.
3.261) says that emissions from biomass used for energy should be noted but not
included in accounting for the Energy sector or other sectors that produce
biomass energy. In other words, emissions from biomass based energy production
are recorded as zero and it is instead assumed that these emissions are
accounted for in the LULUCF sector. Furthermore, unless harvested biomass is
used for energy or industrial purposes or remains in situ it will ultimately
end up in solid waste disposal sites (SWDS) but the waste sector does not
estimate changes in the carbon stock in SWDS. In summary, this means that unless
emissions due to utilisation of harvested biomass are accounted for in the
LULUCF sector, they will not enter accounting at all. Important
emissions will therefore be disregarded and apparent
GHG savings in other sectors (stemming from the use of biomass for energy
production instead of e.g. fossil fuels) may in fact not be real.
2.1.2.
Strengthening synergies with wider policy
objectives
Including the sector in accounting would
also enhance the coherence of the EU's overall climate policy for
several reasons: · Combating climate change is one of the five headline targets of the "Europe 2020" strategy.[8], [9] By reducing
GHG emissions by 20% compared to 1990 levels, increasing the share of
renewables in final energy consumption to 20%, and moving towards a 20% increase
in energy efficiency the EU will contribute to the overall objective of smart,
sustainable and inclusive growth. The Council and Parliament have agreed that
all sectors should contribute to reaching the targets. This is important also
for the EU's role in promoting a level playing field for businesses and a fair
distribution of effort. · Climate change mitigation will continue to play an important role in
the reformed Common Agricultural Policy (CAP). Agricultural emissions of CH4
and N2O from fertilization, livestock and manure are already
reflected in targets (because they are included in the reporting of the
"Agriculture" sector) whereas CO2 emissions and removals
from soils and vegetation are not (since they are reported under LULUCF). Thus,
closely correlated emissions, often occuring on the same land, and sometimes as
a result of the same activities, are not reflected in their entirety in
accounting. The 2010 Communication on the future CAP[10]
outlines how the environmental performance of the CAP could be enhanced through
a mandatory "greening" component of direct payments that supports
environmental measures and give priority to actions addressing climate change
and environmental goals. One benefit which these greening components hold in
common is carbon sequestration, see Table 1. Accounting is necessary for the
efforts invested by MSs, foresters and farmers to be reflected in the EU's
efforts to reach more ambitious GHG reduction targets. Measures to enhance and
protect carbon stocks also demonstrate co-benefits for adaptation, through e.g.
increased water holding capacity and reduced erosion, and for biodiversity
(Natura 2000). · In addition, the provision of renewable energy is an integral part
of the "Europe 2020" targets and the legitimacy of policies that
directly or indirectly promote the use of biomass for energy production, such
as the Renewable Energy Directive (RES-D)[11],
hinges on the correct accounting of resulting emissions and removals. The
accounting of LULUCF can ensure that emissions and removals are correctly
reflected and will balance incentives between different uses of biomass. · Finally, the LULUCF sector forms part of the EU's commitment under
the Kyoto Protocol (KP) in 2008-12. Therefore, whatever reductions in emissions
or increases in removals the EU and its MSs achieve and use for compliance with
that commitment have to be maintained also in future commitment periods.
Indeed, COP16[12] confirmed that LULUCF will continue to count towards Parties'
efforts in future commitment periods and this would need to be mirrored by the
EU in its domestic commitments. Table 1.
Possible carbon sequestration benefits of a post-2013 CAP || Pillar One "Greening Components" || Permanent Pasture || Crop Diversification || Ecological Set-aside || Natura 2000 Description of measure || Protect permanent pasture || Several crops at the same time on the farm || Share of farm area to be devoted to green infrastructure || Assure survival of valuable and threatened species and habitats C sequestration benefits || Maintain and enhance carbon stocks || Increased soil organic matter || Carbon sequestration for permanent crops || Maintain but not increase carbon stores || Grasslands contain three times the carbon in arable land || Potential reduction in carbon losses || Greater benefit if non rotational || Extensive systems = above ground biomass (carbon storage) || Grassland conversion is a hotspot for emissions || Reduced bare soil = reduced carbon loss || Of high benefit if encouraged on organic soils ||
2.1.3.
Preparing the ground for more ambitious targets
There is also scope for economic
efficiency gains from including LULUCF in accounting. The Roadmap for
moving to a competitive low carbon economy in 2050[13] shows
that significant additional climate mitigation efforts will be required across
all sectors, and that agriculture and forestry will become even more important
over time both in terms of preservation and enhancement of carbon stocks and as
a feedstock for energy and material production. Whilst opportunities to remove
additional GHGs from the atmosphere appears relatively limited in the short
term (see Section 5 of this report), the potential cost-efficiency of both
short- and long-term efforts to reach higher targets than the current one would
be compromised if mitigation opportunities are not taken into account.
2.2.
What issues need to be addressed to include
LULUCF in accounting and what is the current state of play?
Whilst there
are good reasons to account for LULUCF in the EU's climate change commitments,
accounting is not trivial. The Council and Parliament require[14] that three issues are
addressed in this impact assessment; in particular (1) how to ensure permanence
and the environmental integrity of the sector's contribution to the
commitments, how to achieve robust (2) monitoring and (3) accounting.
Another important issue is to determine (4) the policy context in which
to include the sector. These aspects are elaborated in this section.
2.2.1.
Addressing the non-permanence of emissions and
removals and the influence of natural effects on emissions and removals
Non-permanence refers to the reversibility
of carbon sequestered in, or released from, the biosphere (Schlamadinger et
al., 2007a). Reversals can be caused by natural disturbances such as fires,
droughts, pests etc. and to some extent storms. Events which may increase in
frequency and magnitude in the future (see e.g. IPCC, 2007), but also as a
result of management decisions, e.g. to harvest or plant trees. The IPCC (2003;
2010) has concluded that there is currently no practicable methodology that can
factor out direct human-induced from indirect human-induced effect and natural
emissions and removals at one point in time. The IPCC GPG has therefore
introduced estimates of GHG on managed land as an approximation of
human-induced emissions and removals. Accounting methods are important to
factor out natural influences between different points in time (see e.g.
Canadell et al., 2007). Fluctuations in emissions and removals in
forests are significant. Figure 2 shows year-on-year changes in emissions and
removals between 1990 and 2008 relative to the total emissions in the
non-trading sectors (regulated by the Effort Sharing Decision; ESD). Whereas
the EU average over the period varies between 1 and 4%, the fluctuations in
individual MSs are as large as 60%, as illustrated by the spread of dots (each
representing a MS) in the figure. The high variability of removed or emitted
GHGs caused by natural phenomena may affect countries' ability to comply with
commitments and raise the question of how to deal with large natural
disturbances in a commitment regime. The inter-annual fluctuations complicates
the possibility of including LULUCF in the commitment on the basis of annual
compliance – a key feature of both the ESD and the EU ETS which currently
regulate the EU's 2020 target. Figure 2. Year-on-year fluctuations in emissions and
removals (in forests remaining forests) over the period 1990-2008 as a
percentage of 2008 emissions in the non-trading sectors (ESD) Note: Each dot
represents the inter-annual fluctuations of a MS in a given year. Percentages
are expressed in absolute terms. Source:
Calculations are based on data from EEA (2011) and the EU ETS data viewer
(2010)
2.2.2.
Ensuring robust monitoring and reporting
GHG dynamics in the LULUCF sector involve
vegetation and soil carbon pools and often a complex web of emissions and
removals (for a detailed description, see Matthews et al., 2011). Their
estimation requires investments in monitoring and reporting capacity. MSs are
obliged to report annually to the UNFCCC on emissions and removals from forest
land, cropland, grassland and all land use changes. The Kyoto Protocol adds
additional reporting requirements for afforestation, reforestation and
deforestation activities and, where applicable, forest management, cropland and
grazing land management and revegetation. Three important aspects of the
current state of monitoring and reporting are discussed below, and more details
are provided in Annex I.
2.2.2.1.
Completeness of estimates
In 2010 reporting under the UNFCCC was
nearly complete for forest land but there were important gaps in reporting for
cropland and grassland (see Table A1.2 in Annex I). At the same time, the
expert review of the first reporting under the KP pointed to the need for
improvements, particularly as regards soil and dead organic matter (JRC, 2011a).
There were differences in reported data between MSs where reporting in the new
MSs was generally less complete than in the EU-15. However, the completeness of
reporting improved in recent years (Ciencala et al., 2010; and JRC, 2011a),
especially for land use changes, likely as a result of the requirements to
report under the KP which is expected to bring about further improvements in
the next few years.
2.2.2.2.
Accuracy of estimates
The IPCC GPG defines accuracy as a relative
measure which means that estimates should not be systematically
over nor under true emissions or removals (see List of
definitions). Countries should seek to provide the highest possible accuracy of
the estimates with the resources available. This is captured by two
methodological concepts: “key category”[15] and
“tier”[16]. A higher tier generally provides more accurate estimates. Key
categories should be estimated with higher tiers (i.e. tier 2 or 3). Usually,
MSs use a combination of different tiers (depending on the land use category or
carbon pool) and higher tiers are more often used in the EU-15 than in the new
MSs. Higher tiers are commonly applied to calculate emissions for forest
biomass, whereas the lower tiers are frequently used for dead organic matter
and soils. Currently, in some MS lower tiers than those required are applied[17]. Another measure of the accuracy of the data
is the level of uncertainty associated with estimates. Based on information
included in MSs’ GHG inventories, the uncertainty in LULUCF is 35%, with
estimates for forest land at 26% and for cropland and grassland combined at
64%, see Table 2. The uncertainty is high compared to e.g. fuel combustion,
transport and industrial processes, but similar to fugitive emissions and
smaller than that of agriculture (non-CO2 GHGs), all which are
already part of the EU's GHG reduction target for 2020. Table 2.
Uncertainty of GHG emission estimates at the EU level Sector || Level of uncertainty (%) Fuel combustion || 2 Transport || 6 Industrial processes || 5 Waste || 21 Fugitive emissions || 32 Agriculture (all categories) || 68 Enteric fermentation || 12 Manure management || 26-61* Rice cultivation || 20 Agricultural soils || 50-157* LULUCF (all categories) || 35 Forest land || 26 Cropland and grassland || 64 Note: * The level
of uncertainty varies with the type of GHG. The estimates are for the EU-15 and indicative, but are
similar for the EU-12 (JRC, 2011a). Sources: EEA
(2010), JRC (2011a) and Leip (2010)
2.2.2.3.
Time consistency and comparability of estimates
The term “consistency” of data means that
the same methodologies and consistent datasets should be used in the
calculation of whole time series. The consistency of data over time within MSs
appears to be reasonably good. Recalculations of reported data are frequent and
sometimes substantial (see Annex I) but as long as the whole time series of
data is updated this is not an issue for time consistency. However, the
variability in tiers applied by different MSs and the existing flexibilities in
definitions (see Annex I and e.g. Lawrence et al. 2010) suggest that data may
not be fully comparable among MSs. For instance, National Forest Inventories
have been developed for different purposes (socio-economic, historical and ecological) and over different
time periods. This has resulted in variations in definitions (e.g. forest,
growing stock volume and land use change) and in methodological approaches.
Official frameworks, exist for comprehensive monitoring of soils in most MSs
Soil inventories exist in very few MS. They are generally heterogeneous in
methodology and coverage, both between and within countries (Arrouays et al.,
2008). However, for the purpose of UNFCCC and KP reporting, the term
“comparability” essentially refers to the fact that countries should use IPCC
methodologies and agreed formats for estimating and reporting inventories.
Compliance by MSs with the IPCC GPG is therefore a priority. In summary, monitoring and reporting
improved significantly in the last years and will likely continue to do so
during the first commitment period of the KP. The current situation is
sufficient for accounting for LULUCF; indeed, it is already being done in the
KP. However, further consideration is needed in terms of how to improve the
reporting of carbon pools, particularly dead organic matter and soils,
especially so in the view of the more stringent reporting requirements for tier
1 level set in the 2006 IPCC Good Practice Guidance. The level of uncertainty
in estimates of emissions and removals is higher than in other sectors (in particular
those regulated under the EU ETS) but notably lower than that of the
agriculture sector which is already part of the EU's climate change
commitments. Recalculations are more common and significant in LULUCF than in
other sectors and make it problematic to incorporate LULUCF in a system of
annual compliance, a requirement which underpins the ESD and EU ETS.
2.2.3.
Ensuring robust accounting rules
Although LULUCF does not yet count towards
the EU's GHG emission reduction target for 2020, it does count towards the EU
and other Parties' commitments under the KP in 2008-2012 and will do so for the
second commitment period. For the first commitment period under the KP, a set
of rules is used to account for different directly human-induced activities:
afforestation, reforestation, deforestation, forest management, cropland
management, grazing land management and revegetation. The rules differ for the
different activities (see Watterson et al. 2011 for a description). They have
been subject to much criticism[18] and, as reflected in the international
negotiations over the last years, there is generally a consensus that
improvements are needed. Rules for the second commitment period,
addressing some of these improvements, was laid down with the draft decision in
Durban in December 2011. However, these rules will apply internationally only
when a second commitment period is finally agreed on and enters into force
through ratification by the Parties. Therefore, when considering the inclusion
of LULUCF in the EU's climate change commitments, the below points and possible
alignment with the revised rules decided upon in Durban need to be considered: · Accounting for forest management does not reflect human-induced
emissions and removals in the first commitment period. Far from all emissions and removals in LULUCF are the result of
human intervention. As noted above (see Section 2.2.1), the anthropogenic and
natural factors cannot be separated at a single point in time. The rules laid
down for the second commitment period address this issue by introducing
accounting using a reference level. A reference level is an estimate of future
emissions or removals based on a continuation of historical emissions and the
effect of the implementation of known measures. Credits or debits are thus
calculated based on whether the actual performance in the sector provides for
more emissions or removals than anticipated. · The provision of incentives for mitigation efforts in forest
management is insufficient. With current accounting
rules, credits are generated as long as a party reports removals that are
greater than emissions, whether or not they are the result of human
intervention. They are limited by a politically established cap. There is no
incentive to mitigate once removals (or emissions) exceed the cap as the
resulting changes will not be counted. This principle will be carried into a
second commitment period also, as it was confirmed in the draft decision on
LULUCF in Durban in December 2011. · Emissions resulting from deforestation may be understated in
accounting. Despite the existence of a cap on
credits from forest management (see above bullet), countries may use removals
above the cap to compensate emissions from debits that result from
deforestation. The maximum allowance (equivalent to 165 MtCO2) for
each Party over the commitment period is very generous, meaning that large
emissions from deforestation may potentially not be accounted for. The
so-called compensation rule was left out of the Draft Decision on LULUCF and
will not apply for a second commitment period under the Kyoto Protocol. · Accounting of emissions and removals is partial and inconsistent. Accounting is currently partly mandatory (afforestation,
reforestation and deforestation) and partly voluntary (any or all of forest management,
cropland management, grazing land management and revegetation). Just over half
of the MSs account for forest management and only three for cropland and / or
grazing land management (see Table 3). Whilst there are some benefits
associated with the voluntary nature of accounting of some activities, e.g.
countries have the option not to take on the burden of reporting and accounting
if emissions and removals are deemed to be insignificant, it also gives little
incentive for parties to select an activity if it is thought to generate
debits. In addition, it introduces inconsistency and limited comparability
between countries in the sense that some MSs account and others not, and
because non-CO2 emissions from managed land must be accounted in the
agriculture sector but CO2 from the same land may be ignored. The
Durban decision made accounting for Forest Management activities mandatory, and
thereby adressed the issue partly. However, accounting for CO2
emissions from agricultural land and wetlands remain voluntary. · Accounting does not always reflect the actual time of emissions. Current accounting rules make the assumption that carbon is
released to the atmosphere instantaneously as a result of harvesting. Whilst
this may be a good proxy for very short-lived products such as biomass that is
used for energy purposes, wood used for more durable products such as sawn-wood
or wood panels used for construction can retain carbon for several decades
(IPCC, 2006). The different life time of products and the time at which
emissions occure are not reflected in the KP rules for the first commitment
period. In Durban the parties agreed that it should be possible to account
gradually for emissions from harvested wood products better reflecting the
actual time of emission. In future reporting, internationally agreed half lifes
of products, or equivalent country specific data of sufficient quality can be
used to calculate the amount of carbon still retained in a given category of
products at a given point in time for each country. Table 3.
Overview of activities that MSs have chosen to account for during the first
commitment period of the KP || Forest management || Cropland management || Grazing land management || Revegetation Member States that have chosen to account for the different activities || Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Poland, Portugal, Romania, Slovenia, Spain, Sweden and the United Kingdom || Denmark, Portugal and Spain || Denmark and Portugal || Romania Total number || 17 || 3 || 2 || 1 Note: Accounting is
mandatory for afforestation, reforestation and deforestation
2.2.4.
Defining the policy context for the inclusion of
LULUCF in the EU's climate change commitment
Once monitoring and accounting have been
addressed, the overarching question is in which policy context the sector
should be brought in to the EU's GHG reduction commitments. The current
commitments are regulated through the EU ETS and the ESD which require trading
sectors to jointly reduce emissions by 21% below 2005 levels and non-trading
sectors to reduce emissions by 10%, see Figure 3. In theory, LULUCF could
either form part of these existing policy frameworks, although the EU ETS as an
option has been discarded early on (see Annex V), or be brought in under a new
framework which is separate from the existing ones. Watterson et al. (2011)
outlines the key features of the ESD and EU ETS that are relevant for the
incorporation of LULUCF. Defining the policy context requires the consideration
of the following issues: · Link to the overall commitment. How
would the link with the EU's overall target be regulated? Would targets be set?
If so, how (taking into account the varying circumstances between MSs)? · Compliance and compliance risk. How
would compliance be defined? How could risks, in particular related to the high
inter-annual variability of emissions and removals in the sector, be mitigated? · Flexibilities. Would credits and debits
generated be transferrable, either between policy frameworks (i.e. EU ETS, ESD
and a potential separate policy framework) or between MSs to create
flexibility? Figure 3. Illustration
of how the EU's current GHG reduction commitment is regulated
2.3.
Who is affected?
The accounting of LULUCF in the EU's GHGs
reduction target would affect MSs directly. Firstly, it would extend the scope
of the accounted emissions and removals by about 10% at the EU level but with
varying importance across MSs. Both natural conditions, relating to e.g.
climate and vegetation cover, and the size of LULUCF relative to other sectors
in Member States play an important role in this regard. For instance, in Sweden
and Finland net removals in LULUCF are more than half the total emissions in
other sectors and in Latvia the double, see Figure 4. This highlights the
importance of considering national circumstances when assessing the role of the
sector in the climate change commitments. Also, accounting rules are of key
importance. For example, accounting for LULUCF in the first commitment period
of the KP is expected to generate credits of about 1% at the EU level, although
there are variations across MSs (JRC, 2011a). Also, MSs would be required to
improve monitoring and reporting in accordance with 2006 IPCC GPG in order to
fulfil obligations. For some MS's some of these data are currently not yet
routinely collected. Additional data collection would be done at country level
as part of national reporting rather than at landholding level, which could
impose an initial administrative burden on authorities and farmers. No impacts
are expected on large businesses or small- and medium-sized enterprises (SMEs). Figure 4.
Relative importance of LULUCF: emissions and removals of the sector relative to
total GHG emissions (excl. LULUCF) in MSs, 2008 Note: A negative
number indicates that removals are greater than emissions in LULUCF for that
Member State. (2) Due to inter-annual variations in emissions and removals the
share varies between years. Source: EEA (2011) Secondly, it would adjust accounting to
better reflect real changes in emissions and removals and to provide a level
playing field between different mitigation options (i.e. sequestration /
conservation in carbon in vegetation and soils in agriculture and forestry vis-a-vis
substitution of materials with biomass in the energy and industrial sectors).
In principle, this would affect agriculture and forestry and users of biomass
for energy production, manufacture of pulp, paper and paper products,
manufacture of wood and wood products or substitution of materials in
construction. However, the extent to which these different sectors would be
affected depends on at least three factors; firstly, how accounting rules are
formulated, secondly, whether a target for LULUCF is introduced (either sector
specific or more indirect via the ESD) and, thirdly, whether MSs translate the
accounting framework into incentives at sector level (or else the impact will
be on government expenditures and revenues).
2.4.
How would the problem evolve, all things equal?
As noted in Section 2.1 above, omitting the
sector from accounting would risk undermining the environmental integrity of
the commitments, reducing the coherence of EU climate policy and limiting the
economic efficiency in reaching more ambitious targets. This section elaborates
on how these aspects might evolve further if no action is taken. Figure 5 shows the results of the projected
emissions and removals in the reference scenario (see Annex IV for more
detailed results of the projections and Annex III for the models and
methodology used). Models project a decrease in the EU by 2020 under a
business-as-usual scenario.[19]
For the LULUCF sector as a whole, a decline of about 10% is expected in 2020
compared to the period 2005-2009, equivalent to emitting 33 MtCO2
more per year. This roughly amounts to all greenhouse gas emissions in Latvia
and Lithuania put together or twice those of Estonia in 2009. A closer look at this projection shows that
there are big differences between the individual activities within the sector.
The decrease is expected to be very pronounced in forest management, for which
a decrease in net removals of about 60 MtCO2 is expected, i.e.
roughly the equivalent of the total GHG emissions of Bulgaria, Denmark, Ireland
or Sweden in 2009. Whilst this is partly the result of ageing forests (and
related saturation in CO2 uptake) it is also partly the effect of EU
and MS policies, notably the expected impact of increased wood demand related
to reaching the targets for renewable energy (Böttcher et. al, 2011, p. 19).
This is partly compensated by plantation of "new" forests
(afforestation). Emissions and removals from agricultural activities such as
cropland management and grazing land management are expected to remain fairly
stable or to improve. The predicted trend is consistent with findings of other
studies, see Box 2, and illustrates the increasing importance of the problem
discussed under environmental integrity; negative trends and emissions
risk being disregarded unless LULUCF is part of climate policy and accounting. Figure 5.
Projected emissions and removals in LULUCF 2000-2020 Notation key:
●–●–● LULUCF (sum of all activities), ▲–▲–▲
Deforestation, +–+–+ Cropland management, ––– Grazing land management,
♦–♦–♦ Afforestation, and ■–■–■ Forest
management. Unconnected points show reported / historical data. Note: (1) A
negative number indicates that removals are greater than emissions for that
activity. (2) Instant oxidation is assumed for the harvested wood products
(HWP) pool. If this pool were included the projected forest management curve
would shift downwards (increased removals) by approximately 55 Mt in the period
2013-2020 and by a similar amount for reported data (Rüter, 2011). Source: Böttcher et
al. (2011) and JRC (2011b) Box 2. How do
the model results in this report compare with other studies? In 2011 MSs and other Parties
to the UNFCCC submitted projected emissions and removals from forest management
to facilitate the decision on accounting rules at the global climate change
conference in Durban in december this year.[20]
Some 10 MSs used country-specific models but 15 MSs based their submissions on
the models used for this report (see Annex IV for more detailed results of the
projections and Annex III for the models and methodology used here). The
decline in the sink in Figure 5 is consistent with (although smaller than) the
results obtained when combining the country-specific model results with the
ones used here. Also the expected increase in wood demand for both material and
energy use (i.e. a key driver for the evolution of the sink) is consistent with
work undertaken by Mantau et al. (2010) The magnitude of potential supply in
the latter study varies with assumptions about environmental, social and
economic factors. As shown in the Roadmap for moving to a competitive
low-carbon economy in 2050 (Op cit.), this trend is expected to continue in the
long run and the role of agriculture and forestry in energy production is
expected to increase. The projected development of the sink would
increasingly affect policy coherence and economic efficiency. If
the decline in net removals stemming from non-action remain unaccounted for
this would risk disincentivizing preservation and enhancement of carbon stocks
and thereby creating an unlevel playing field between different mitigation
options, that could result in an excessive use of resources for mitigation
measures that count towards other objectives (substitution of fossil fuels in
energy production and of GHG intensive materials in industry). This risks
resulting in economic efficiency losses that may increase over time given that significant
additional efforts will be required in all sectors to meet the long-term
climate objectives.
2.5.
Does the EU have the right to act and is EU
added-value evident?
The legal basis for this initiative is
Article 192(1) of the Treaty on the Functioning of the European Union (TFEU).
Article 9 of the ESD, also based on Article 192(1) TFEU, tasks the Commission
with assessing if and how LULUCF should be included in the EU's GHG reduction
commitment, and to make a legislative proposal, as appropriate. Climate change
is a trans-boundary issue which requires joint action by countries. In
particular, since the EU has a common emission reduction target any changes
will require Union wide action. The principles of subsidiarity and
proportionality have been duly considered.
3.
Objectives
3.1.
What are the general and more specific /
operational objectives?
The general objective of the proposal which
accompanies this impact assessment is to support the EU's ambition of limiting
global warming to a maximum of two degrees Celsius above pre-industrial
temperatures. The EU's short term commitment for 2020 is to reduce its overall
GHG emissions by at least 20% below 1990 levels. As agreed by the European
Parliament and Council, the contribution of all sectors should be reflected in
the efforts of reaching the target. For this objective to be achieved, the
emissions and removals related to LULUCF would need to be reflected in
accounting. However, they must be included so as to address the issues
identified in Section 2.2; namely to ensure permanence and the environmental
integrity of the EU's GHG reduction commitments as well as accurate monitoring
and accounting. The correct policy context also needs to be defined. To this
end, the following operational objectives have been identified: · Monitoring and reporting
activities undertaken by MSs should comply with the latest IPCC methodological
guidance approved by UNFCCC to ensure the transparency, completeness,
consistency and comparability and accuracy of estimates; · Accounting rules should: (1)
be extensive in terms of emissions and removals
covered and, to this end, shall include at least all main LULUCF activities
(afforestation, reforestation, deforestation, forest management, cropland
management and grazing land management)[21],
(2)
ensure that the non-permanence of emissions and
removals is fully reflected in accounting, (3)
provide incentives for climate change mitigation
whilst avoiding bias towards any particular measure to ensure an appropriate
balance of measures in the contribution to climate change mitigation,[22] · The policy context in which LULUCF is
included should be such that the ability of MSs to comply with GHG reduction
targets is not put at risk due to inter-annual variability of emissions and
removals or large natural disturbances.
4.
Policy options
4.1.
What are the possible options for tackling the
problem and meeting the operational objectives?
The problem
definition shows that accounting for the sector would enhance the environmental
integrity, policy coherence and economic efficiency of the EU's GHG emission
reduction commitments. In the light of this, different objectives have been
defined. Options can be divided into two levels to address, on the one hand,
the objective related to the policy context and, on the other hand, the objectives
related to accounting and monitoring and reporting. Firstly, it is
necessary to define the policy context in which the sector should be
accounted since there is already legislation in place to regulate the existing
commitment to reduce GHG emissions by 20% in 2020. Three options have been
defined. Option 2 involves creating a legislative framework for LULUCF which is
separate from the existing frameworks provided by the ESD and the EU ETS. This
option was divided into one without targets (Option 2.I) and one with targets (Option
2.II). It should be noted that the target option was included in the
analysis for illustrational purposes only and to initiate discussions. Option 3
involves including LULUCF in the existing legislative framework of the ESD. The
EU ETS as an option was discarded at an early stage (see Annex V for further
details). It is good practice to consider also a non-regulatory / "no EU
action" option (Option 1). Secondly,
different sub-options have been developed to assess how robust accounting
(options (a) to (c)) and monitoring and reporting can be
achieved. The sub-options are the same for Options 2 and 3 but their impacts
differ depending on the policy context. Figure 6 illustrates how the two levels
of options relate to one another. A summary of stakeholder views on the
different options is given at the beginning of each section to explain why
particular options have been included in the assessment. Figure 6.
Outline of options
4.2.
Options for the policy context in which to
include LULUCF in the EU's GHG emission reduction commitments
This section describes the options for
including LULUCF in the EU's commitments as regards the policy context. Option
1 involves no EU action. The implementation of Options 2 and 3 are described in
terms of their link to the overall commitments, compliance and compliance risk
and flexibilities. Both options would require new or amended legal acts. What are the views of stakeholders? The majority in both the consultation with the public and with MSs responded that LULUCF should be part of the EU's GHG emissions reduction target in 2020. In both consultations, but most notably in the consultation with MSs, there was a majority in favour of making the sector part of the target only if the EU were to take on a more ambitious commitment than the current one of a 20% reduction compared to 1990. In the public stakeholder consultation there was a slight preference for regulating the role of LULUCF in the climate change commitments through a policy framework which is separate from those that currently regulate the EU's GHG reduction commitment (i.e. the ESD and the EU ETS), preferably with a sector target of some sort. The option of regulating the sector through the ESD was second. The reverse was true for the MS consultation where the ESD was the favoured option. Only a small share of the respondents in the public consultation and none in the MS consultation wanted the sector to form part of the EU ETS. See Section 1.3 for links to more detailed information.
4.2.1.
Option 1 – No EU action
This option involves not accounting for
LULUCF in the EU's climate change commitments at all. In practice, this is only
realistic in the absence of an international agreement; given that the EU is a
Party to the KP, any commitment there would have to be shared between MSs and
would necessitate a common approach to both accounting rules (determined
internationally) and to how debits and credits would be treated in relation to
existing commitments under the ESD and the EU ETS. Therefore, "do nothing"
really translates into delaying all action until an international agreement on
a second commitment period has been reached.
4.2.2.
Option 2 – Include LULUCF as a separate policy
framework
This option involves establishing a policy
framework which is separate from the ones currently regulating the EU's GHG
reduction target (the ESD and the EU ETS). Emissions and removals would be
accounted for at the level of MSs. In terms of linkages to the EU's climate
change commitments, two sub-options are considered. Sub-option 2.I – No mitigation targets: The EU is already on track to achieve its 20% reduction target of
GHG emissions without the contribution of LULUCF. Before the level of ambition
is increased beyond that target conditions need to be right. In Sub-option 2.I
credits and debits generated by LULUCF would therefore not count towards the
current level of the EU's commitments. As a first step, the objective would be
to ensure that emissions and removals from the sector will be properly
accounted for. Any credits or debits generated in LULUCF would be treated as a
memo item. However, as a second step, and as part of a step-up of the EU's
climate change commitments beyond 20%, a specific target for the sector to
increase net removals or reduce net emissions could be envisaged as illustrated
by Sub-option 2.II below, or the sector could be included in Option 3. As an
intermediate step, action plans would be formulated by MSs to ensure that
mitigation action is taken in the sector. Sub-option 2.II – Sector mitigation
targets (included for illustrational purposes only):
Targets could be set as part of an economy-wide step-up of efforts beyond 20%.
This impact assessment does not analyse targets that support a legislative
proposal to this end. Rather, the option of setting targets is analysed in a
preliminary way and only to initiate discussions. If targets were to be
introduced at a later stage, a similar approach to the ESD could be adopted
such that targets set for individual MSs would be fair and achievable. Credits
and debits generated by LULUCF would count towards the EU's GHG reduction
commitment in accordance with agreed accounting modalities. Targets to increase
net removals (or decrease net emissions) could be set explicitly for individual
MSs based on one of several parameters; for instance the estimated mitigation
potential in LULUCF (underpinned by modelling), GDP per capita, or a uniform
percentage change compared to business-as-usual projections. However,
sufficient flexibilities would have to be ensured to minimise adverse risks of
the uncertainty in estimates (see Section 2.2.2 and Annex III) which would
otherwise have implications for reaching targets. For illustrational purposes
only, this report takes a closer look at the possible impact of introducing
targets based on mitigation potential, underpinned by modelling (see Annexes
IV). Flexibility mechanisms are only relevant in the case of Sub-option 2.II where they would
safeguard cost-efficiency and ensure that a failure to meet sector-specific
targets could be compensated by efforts in other sectors to preserve the
overall EU target. This could be allowed through purchases and sales of LULUCF
credits among MSs to facilitate their compliance with LULUCF targets, or
through compensation with credits resulting from over-compliance in the ESD.
Again for illustrational purposes only, this report looks at the possibility of
transferring LULUCF credits between MSs. As regards compliance (again
relevant when accounting towards a target), Section 2.2.1 shows that the
inter-annual variability of emissions and removals is very high. This makes
annual compliance unsuitable (see the discussion in Option 3 for more details).
Instead, compliance would be based on the average of emissions and removals
over the period 2013 to 2020. Annual reporting would still be required as
currently done under the UNFCCC and the KP. Compliance risk due to large
natural disturbances would be addressed by way of specific accounting
provisions (see below).
4.2.3.
Option 3 – Include LULUCF in the Effort Sharing
Decision
This option involves amending the ESD in
order to include LULUCF among the sectors covered by existing commitments.
Hence, liability for both emissions and removals would fall on MSs. An overview of the key features of the legislation is provided in
Watterson et al. (2011). In terms of linkages to the EU's climate
change commitments, the ESD specifies binding targets for MSs between plus
/ minus 20% in 2020 relative to emissions in 2005, estimated on the basis of
GDP per capita. If LULUCF were to be included, the distribution of individual
MSs' efforts would have to be adjusted accordingly.[23] In theory, this could be done
by changing the MS targets (based on GDP per capita) on the basis of the
estimated mitigation potential in LULUCF, underpinned by modelling (see Annexes
VI and VII). Or, given sufficient flexibilities, the targets could also be
adjusted based on simpler metrics such as a uniform percentage change compared
to business-as-usual projections. The most straightforward option is to
maintain the current targets based on GDP per capita. This approach would mean
that the challenge for MSs to reach their targets would most likely change with
the inclusion of the sector, the reason being that diverse natural conditions
relating to e.g. climate, vegetation cover, age of forests and soil throughout
the EU play an important role in the mitigation potential of MSs. Given the
difficulties associated with including LULUCF in the ESD in its current form,
particularly in relation to the requirements of annual compliance (see
discussion in Section 2.2 and below), the present analysis is limited to the
impacts of maintained targets only. The ESD contains different flexibility
mechanisms. Within a MS, over-achievement during 2013-19 can be carried
over to the subsequent years, up to 2020. Up to 5% of the annual emission
allocations of the following year may be brought forward during 2013-19. A
transfer of up to 5% of annual emission allocations for a given year can be
transferred between MSs. Such a transfer may be used for the implementation of
a MS's obligations for the same year or any subsequent years until 2020. These
flexibilities would continue to apply if LULUCF were included in the
commitment, but the possibility for borrowing and lending between years would
need to be significantly expanded in several MSs due to the inter-annual
variability of emissions and removals. In terms of compliance, MSs are
required to annually limit their GHG emissions, in accordance with a linear
trajectory for 2013-2020. This includes also making use of specified
flexibilities in order to make sure that emissions do not exceed their limit in
2020. The starting point for the trajectory is the average emission level in
2008-2010. To address some compliance risks, there are provisions for extreme
meteorological events so a MS may request an increased carry-forward rate in
excess of 5% in 2013 and 2014 if substantially increased GHG emissions occur in
those years compared to normal years. This provision would not be adequate if
it were to apply also to large natural disturbances such as wildfires and
storms. It would be difficult to recover emissions related to such events
through removals, both because of the short time span and because of the high
global warming potential of non-CO2 emissions associated with fires
which would take a long time to recover (since removals of CO2 is
the only way to compensate). As this provision was designed for sectors other
than LULUCF, changes would be needed to address disturbances (see accounting
options below).
4.3.
Options for improving monitoring and reporting
This section outlines an approach for
meeting the monitoring and reporting objectives defined in this report. In
practice, the implementation would involve revising the Monitoring Mechanism
Decision[24].
A separate impact assessment and proposal from the Commission address this
issue but complementary information is provided here. What are the views of stakeholders? Whilst both the public consultation and the consultation with MSs have shown that the majority favours an inclusion of LULUCF in the EU's GHG commitment, a common reason amongst those who did not wish to include the sector related to perceptions about the reliability and comparability of data. This suggests that improvements to monitoring and reporting may be needed.[25] In both the public and MS consultations the majority agreed that there is a need for further harmonisation and standardisation between MSs in terms of monitoring, reporting and verification of emissions and removals. See Section 1.3 for links to more detailed information. There is no unique way of reaching the
objective because the IPCC methodological guidance allows for different
methodologies and approaches to be applied according to national circumstances.
For instance, a number of tools exist for representing land areas (e.g. use of
existing data, remote sensing and ground-based surveys) and for deriving
emission factors (e.g. default values, literature, measurements and models),
and these can be combined in a number of ways. However, in a broader sense, a
three-step approach can be envisaged (see Annex I for more details). A first step is to ensure complete
reporting of land categories and carbon pools using at least simple
methodologies. The objective of this step is to ensure the level of
completeness stipulated by the IPCC / UNFCCC to a minimum level of tier
1. Fulfilling this objective require increased collection of activity data. A second step would involve increasing the
accuracy of the reported key categories and carbon pools to a minimum of tier
2. Tier 1 methodologies only distinguish between the broad categories of land
management, which means that the effects of some of the available mitigation
activities are not reflected in the inventories. This step would therefore
mainly imply requirements to improve the level of accuracy of monitoring and
reporting of emissions and removals related to soils as this is where most
efforts are needed. This step would ensure the completeness stipulated by the
IPCC methodological guidance and a methodology consistent with key category
analysis.[26]
Moving to tier 3 (including modelling) would be encouraged to further increase
accuracy (and in some cases for cost efficiency), but it is not an essential
requirement of Step 2. A third step would aim to improve the
comparability between MSs by harmonising monitoring, reporting and related
nomenclature. Cienciala et al. (2010) investigated harmonised methods for
assessing carbon sequestration in European forests. Tomppo et al. (2010) showed
that harmonisation is possible and on-going in COST Action 43, which reviewed
the potential for common reporting in the context of National Forest
Inventories. In addition, Kibblewhite et al. (2008) have described requirements
for a harmonised soil monitoring system in the EU. Steps 1 and 2 would go some
way towards harmonisation. However, processes leading to change in inventory
practices, such as acceptance of new definitions, are usually slow. McRoberts
et al. (2010) concluded that whilst important differences in national forest
inventories remain, because of the use of different national definitions and
methodologies, prospects are generally positive for developing procedures
leading to compatible estimates among countries. This step should be seen as a
gradual process which will need to take place over the next compliance period
and beyond and could involve collaborative efforts between the Commission and
MSs through workshops and working groups.
4.4.
Options for accounting
This section outlines different options for
meeting the objectives related to accounting. Consultations carried out with a
range of stakeholders and MSs and on-going discussions under the auspices of
the UNFCCC suggest that there are three main options to account for emissions
and removals resulting from afforestation, reforestation, deforestation, forest
management, cropland management, grazing land management, revegetation, changes
in the harvested wood products (HWPs) pool, and disturbance events. The
practical implications of implementing either of the options would be to define
the accounting modalities in EU legislation. What are the views of stakeholders? In the public stakeholder consultation, the majority of respondents suggested that emissions and removals related forestry activities should be accounted for on a mandatory basis. About half wanted accounting for GHG associated with some agricultural activities to be mandatory. The tendency was similar across MSs, but few wanted agricultural activities to be included on a mandatory basis. As regards how accounting should be done, in particular for forest management, the public consultation showed that there were two dominant groups; one that thought that changes in emissions and removals should be measured against a benchmark of some sort, either emissions in a base year (1990) or a business-as-usual projection ("reference level"), and one group that believed that no benchmark should be used (meaning that credits would be generated as long as forests remove more carbon from the atmosphere than they emit, regardless of the trend in emissions and removals). MSs felt that accounting for changes against a benchmark ("reference level") was the most appropriate option. See Section 1.3 for links to more detailed information. Table 4. Summary
of accounting options Activity/Provision || Accounting rules by option Accounting option (a) Small changes to the current KP rules || Accounting option (b) Likely outcome in the UNFCCC negotiations || Accounting option (c) UNFCCC+ Afforestation and reforestation || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory Deforestation || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory || Accounting for emissions and removals without a benchmark (so-called "Gross/Net accounting") Mandatory Forest Management || Accounting without a benchmark (so-called "Gross/Net accounting") but emissions and removals are discounted by 85% Mandatory || Accounting relative to a reference level based on business-as-usual projections for 2013 to 2020 Mandatory || Accounting relative to a reference level based on business-as-usual projections for 2013 to 2020 Mandatory Cropland Management || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Mandatory Grazing Land Management || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Mandatory Revegetation || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary || Accounting for emissions and removals relative to 1990 (so-called Net/Net accounting) Voluntary Changes in the Harvested Wood Products carbon pool || Accounting for emissions from harvested wood at the time of harvest || Accounting for emissions from harvested wood at the time they actually occur (depending on the lifetime of wood products) || Accounting for emissions from harvested wood at the time they actually occur (depending on the lifetime of wood products) Natural disturbances || No specific provisions for emissions related to natural disturbances || Emissions from natural disturbances exeeding a background level, may be excluded from accounting Voluntary || Emissions from natural disturbances, exceeding a background level, may be excluded from accounting Voluntary Note: Shaded areas
indicate a change from the accounting rules under the Kyoto Protocol in the
period 2008-12. In Table 4 above, shaded areas indicate
where the options differ from the accounting rules under the KP in the period
2008-12. A more detailed explanation and rationale for the different rules is
given under each option. Background reading is provided also by Watterson et
al. (2011). Some options, such as accounting for the specific activity of
rewetting and drainage on land with organic soils, were excluded at an early
stage for the reasons given in Annex V. It should be noted that where cropland
and grazing land management are accounted for the vast majority of emission
from organic soils are captured anyway.
4.4.1.1.
Accounting option (a): Small changes to the
current Kyoto Protocol rules.
In this option, accounting for forest
management would be done on the basis of so-called Gross/Net accounting and
discounted at a rate of 85%. This means that credits (debits) would be
calculated as 15% of all net removals in the commitment period without
consideration of net removals in a base year or period, i.e. no benchmark. In
the example given in Figure 7, this translates to credits equal to
"b" which is a fraction (15%) of "a". As discussed in
Section 2.1, in the absence of a benchmark it will be impossible to separate
anthropogenic from natural factors but the discount factor is a proxy for the
share of human-induced emissions and removals. This accounting option for
Forest Management is no longer on the table in the international negotiations. Afforestation and reforestation involve establishing forests on land that was not forested at the
very beginning of 1990, and deforestation involves converting areas,
which in 1990 were covered by forest, for other uses or activities. Emissions
and removals related to these activities in the commitment period would be
accounted for in their entirety ("Gross/Net accounting") as they are
a direct result of human activities (i.e. it is possible to determine
human-induced impacts). Figure 7.
Illustration of accounting methods Note: Gross/Net
– Credits are equal to all net
removals during the commitment period = "a"; Gross/Net with a discount factor – Credits
are equal to all net removals
during the commitment period and discounted by a factor of X% = " b";
Net-net compared to 1990 –
Credits are equal to the
difference between the removals during commitment period and the net removals
in 1990 = "c". As under the accounting rules of the first
commitment period of the KP, accounting for emissions and removals in cropland
and grazing land management and revegetation would be voluntary and
done on the basis of changes relative to 1990 (see "c" in Figure 7).
This means that credits (debits) would be generated to the extent that net
removals are higher (lower) in the commitment period as compared to the base
year. Soils have different characteristics to biomass and the inter-annual
variations in cropland and grassland are much smaller than those of forests.
This means that accounting will not be as sensitive to the choice of base year
(cf. fluctuations in forest management in Figure A5.1 in Annex V).
4.4.1.2.
Accounting option (b): Likely outcome in the
UNFCCC negotiations
This accounting option reflects aligning to
the outcome on accounting rules in the UNFCCC negotiations in Durban in
December 2011. It differs from accounting option (a) in three ways. Firstly,
accounting for emissions and removals in forest management is done
relative to a reference level in order to factor out the impact of natural
influences on removals and emissions, see Box 2 and Figure 8. The reference
level is combined with a quantitative limitation ("cap") on credits
corresponding to 3,5 % of base year emissions (excluding LULUCF) to limit the
effects of the inherent uncertainty of (any) projections and to avoid that
unwarranted credits arise. The quantitative limitation is expressed as a
percentage of a MS's total GHG emissions in 1990 in order to ensure the link to
compliance risk (because GHG reduction targets are expressed relative to this
year). The cap ensures that some incentives for improvements in management
practices are provided., see Annex III for more details on the impacts of
different caps. Figure 8. Accounting
emissions and removals in forest management against a reference level Note: Accounted
emissions and removals are equal to the average of actual net removals in the
commitment period minus the projected emissions and removals under
business-as-usual, the so-called reference level. Credits are obtained when
actual net removals exceed the reference level, and debits are obtained when
actual net removals are less than the reference level. Box 2.
Accounting for forest management on the basis of a reference level What is a reference level? The reference level is set as the average of the projected removals and emissions under business as usual in the period for which the EU commits to reduce its overall emissions, in this case the period from 2013 to 2020, see Figure 8. In essence, accounting against the reference level means that a Member State will receive neither credits nor debits if the average of actual net removals in the compliance period equals the reference level, but that if net removals are higher (lower) this will result in credits (debits). In fact, in response to a decision[27] in the Cancun Agreements, Member States submitted numbers for reference levels to the UNFCCC for review by expert teams for consideration prior to the decision on accounting rules at the Conference of Parties in Durban last December. The outcome of the review process (http://unfccc.int/bodies/awg-kp/items/5896.php) has been assessed by the CMP7. The reference levels take into account the effect of removals or emissions from forest management as shown in greenhouse gas inventories and relevant historical data; forest characteristics such as age class structure, increments and rotation lengths; and domestic policies implemented and adopted no later than June 2009. Importantly, the cut-off date means the expected impacts on harvesting of reaching the renewable energy targets (as set out in Directive 28/2009/EC) are not included in the reference levels. What is the rationale behind reference levels? Emissions and removals depend on a number of natural (regional/geographical) circumstances such as variations in growing conditions (temperature, precipitation and droughts) and natural disturbances (storms, fires) as well as past and present management practices (e.g. rotation lengths which affect the distribution of age classes in forest stands) and therefore the rate of removals. Ultimately, the capacity of forests and soils on a given area of land to remove carbon from the atmosphere will saturate because a steady state will occur in the balance of emissions and removals. By measuring changes in emissions and removals relative to business-as-usual projections these circumstances are "factored out" so that only changes related directly human-induced activities are measured. This also provides incentives for improving on the current situation and gives an equal value to mitigation whether through sequestration or conservation or material and energy substitution. Secondly, provisions for disturbance
events would be needed to deal with substantial natural disturbances that are
beyond the control of and not materially influenced by MSs and the resulting
compliance risks, see Box 3. Box 3.
Accounting for natural disturbances In the case of extraordinary occurrences whose associated total annual GHG emissions and removals are a minimum of e.g. 3-5% per cent of the total national emissions (excl. LULUCF) in 1990, it should be possible to exclude part of the associated emissions from accounting. The trigger for the application of these provisions would be defined in terms of base year emissions because it is designed to address compliance risk with the EU's climate change commitments (which are determined on the basis of total GHG emissions in 1990). MSs would be able to exclude part of emissions from the lands affected minus any subsequent removals on those lands until the end of the commitment period. However, in the case of land-use change following substantial natural disturbances, MSs would not be able to exclude emissions. The application of this provision would be voluntary but requires solid and evidence-based justification[28]. In the rules laid down for the second commitment period under the Kyoto Protocol, a corresponding provision is set that includes the use of a background level to be calculated fro each party. The 3-5% mentioned above, can be seen as such a background level. Carbon is stored in various
"pools", primarily in above- and below-ground biomass, such as wood,
leaves, roots, and in soils (see Matthews et al. (2011) for more details). But
carbon is also stored in products made from harvested wood. In the KP rules
emissions are accounted as if they occur at the time of harvest. However,
harvesting does not generate emissions per se, it is a transfer of carbon from
one pool (e.g. above-ground biomass) to another (harvested wood products). In
accounting option (b) and (c), emissions would instead be accounted for when
they occur, as also decided for the next KP commitment period by COP17/CMP7 at
the Durban meeting in December 2011. In accounting option (b) and (c) and in
the next commitment period, sawn wood, wood panels and paper would be accounted
for on the basis of exponential decay (i.e. loss) or emission with default
half-lives of 35, 25 and 2 years respectively. Wood that is used for energy
production, which is generally combusted within a year, and wood put in
land-fills would still be accounted for at the time of harvest. Net removals
will occur as long as the transfer of carbon to the harvested wood products
pool is greater than the loss from the pool. The Durban decision allows for
substitution of the default half lives values, where transparent and verifiable
country specific data are available.
4.4.1.3.
Accounting option (c): UNFCCC+
This accounting option is based on the outcome
in the UNFCCC negotiations in Durban (Accounting option (b)) but goes further
in addressing the criticisms against the accounting rules under the first
commitment period of the KP. In particular, the scope of accounting and
coverage of emissions and removals would be extended to accounting for cropland
management (already accounted for by Denmark, Spain and Portugal) and grazing
land management (accounted for by Denmark and Portugal) on a mandatory
basis. Regardless of the outcome of the ongoing international negotiations, the
EU (as a Party) is able to decide whether to account for all activities. Such a
choice would be fully compatible with the international system.
4.5.
Which options have been discarded at an early
stage and why?
Annex V contains a description of discarded
accounting options. In terms of defining the appropriate policy
context, the annex summarises the key points for not considering the
inclusion of LULUCF in the EU ETS in this impact assessment, as discussed in
three earlier studies[29].
5.
Analysis of impacts
This section
presents an analysis of the impacts related to the different options presented
in Section 4. It is in the mandate of this impact assessment to address how
LULUCF may be included in the EU's overall GHG reduction target. The
environmental, macro-economic and social implications of, in particular,
different accounting options depend on if and how LULUCF is linked to the
overall climate change commitments. Therefore, this section brings together the
analysis of accounting options and the steps for improving monitoring and
reporting with the different overall policy options (cf. Figure 7) of (1)
taking no action, (2) accounting for LULUCF in a separate framework I) without
mitigation targets and II) with mitigation targets, and (3)
including LULUCF in the ESD. As noted in Section 4, the analysis of sub-option
2.II, which sets mitigation targets for LULUCF, is preliminary and serves as a
basis for discussion only. Impacts are assessed relative to the
reference scenario outlined in Section 2.4 (and elaborated in Annexes III and
IV) as the most likely outcome in the absence of policy interventions.
5.1.
Environmental implications
5.1.1.
Impact of the different options on GHG emissions
5.1.1.1.
Option 2.I – Separate framework for LULUCF
(without mitigation targets)
The aim of this option is to introduce and
improve accounting, monitoring and reporting methods in the EU's GHG reduction
commitment without changing the agreed efforts already introduced under the
Climate and Energy Package. In other words, it does not involve revising
targets upwards or downwards and rather records emissions and removals as a
memo item. This would prepare the ground for a more inclusive step-up of the
EU-wide efforts in the pursuit of the long-term targets when conditions are right.
Table 5 presents the expected credits and debits per year over the period
2013-20 in the reference scenario, i.e. without additional mitigation efforts.
The level of credits is uncertain because of uncertainties in the emission
inventories and in the projections in particular in relation to future wood
demand (including the demand for biomass energy) and the possible role of
natural disturbances (see Annex III for a description of uncertainties). Accounting option (a) is expected to result in credits of around -79 MtCO2 per
year for the EU as a whole This adds up to credits of some 629 MtCO2
over the whole commitment period. The main reason is that credits in existing
forests are calculated without a benchmark, i.e. all net removals in the
commitment period are converted into credits regardless of whether they are the
result of changes in management practices or natural factors and then
discounted by 85%. This approach would create credits which are largely
unrelated to actual management practices, i.e. windfalls. It would also reduce
the incentive for mitigation efforts in the sector as each tonne of CO2
mitigation achieved at any price level would be discounted and, hence, made
more expensive relative to mitigation measures in other sectors. Estimates show
that accounting option (a) is expected to generate credits for most MSs or have
hardly any impacts at all, see Table A4.4 in Annex IV for more details. Table 5. Estimated annual credits (MtCO2 per
year) in 2013-20 for the three accounting options MS || Accounting option (a) || Accounting option (b) || Accounting option (c) Small changes || Outcome of the UNFCCC negotiations || UNFCCC+ || || (I) || (II) || (I) || (II) Annual average over commitment period || -78.7 || -14.5 || -85.8 || -35.0 || -106.3 Cumulative 2013-20 || -629 || -116 || -686 || -280 || -850 Notes: Negative
values denote net credits (and positive values net debits). Options take into
account that some MS already have voluntary accounting of GM and FM in the
baseline. Credits for Forest
management are calculated in two different ways (in both cases, the impact of
the cap on credits equal to 3.5% of base year emission is included): (I) (modelled emissions and removals in reference scenario) - (modelled emissions and removals in baseline scenario) (II) (modelled emissions and removals in reference scenario) - (forest management reference levels in LULUCF decision of CMP.7) Differences between (I) and (II) reflect different assumptions or methods used in the models' projections as compared to country-specific projections. Source: Calculations
based on Böttcher et al. (2011) and JRC (2011b) and, for those MSs that based
their submissions of reference levels on this work, have been adjusted
following the expert review of forest management projections under the UNFCCC. Accounting option (b) is expected to result in credits of around 10 MtCO2 per
year and 77 MtCO2 cumulatively over the whole commitment period if
credits are calculated using model projection for forest management reference
levels ((b(I)). Credits would be nearly 86 MtCO2 per year and 686
MtCO2 cumulatively over the whole commitment period if the forest
management reference levels agreed in UNFCCC decision CMP7 would be used
(option b(I). For 10 countries the decision uses forest management reference
levels projected by member states rather than model projections.Under option
(b) credits are substantially smaller than option (a) because this option
measures changes in emissions and removals relative to a reference level. In
fact, debits are expected from forest management as a result of an increasing
demand for wood for energy purposes. When provided for energy purposes such
wood will avoid CO2 emissions in the energy sector while
substituting fossil fuels. The overall credits of the sector are explained by
net removals from afforestation efforts since 1990 which exceed the expected
emissions from deforestation. There are significant variations between Member
States (see Table A4.4 in the Annex for details). For instance, estimates show
that 17 countries (e.g. Germany, Poland, UK, Austria and France) would have
credits whereas in ten countries (e.g. Italy, Spain and Romania) debits may
arise. Accounting option (c) (UNFCCC+) results in expected credits of 35 MtCO2 per
year with modelled projections (column I) and 106 Mt using the reference
projections submitted to the UNFCCC (mix country and model projections).
Overall credits are higher than in option (b) because this option includes the
effects of mitigation achievements in cropland and grazing land management. The
latter activity in particular is expected to lead to an increase in net
removals compared to 1990. The cumulative credits for the commitment period
would be 315 MtCO2. The distribution of debits and credits amongst
MSs is similar to that in option (b). Contrary to option (a) which is based on
discounting, both options (b) and (c) fully reflect changes in forest
management practices (1 ton change = 1 ton accounted), here in response to the
expected increase in demand for wood for energy purposes. The results also
clearly illustrate the difference between accounting against a reference level,
where only changes in management practices will count, and accounting for
emissions and removals without any benchmark. In particular, option (b) and (c)
ensure that the use of biomass in other sectors than LULUCF is mirrored in
accounting and therefore provides a level playing field between different uses
of biomass. The expected increase in the use of biomass for energy associated
with reaching the RES-D targets is estimated to involve a reduction in net
removals of on average about 15 MtCO2 per year in the period
2013-2020 (based on the JRC LULUCF tool calibrating model results see JRC
(2011b) and Böttcher et al, 2011, p.39). One should recall that such biomass
allows to save GHG emissions in the energy sector while replacing fossil fuels.
Finally, the effects of the more inclusive accounting in option (c) is
reflected in the higher level of net removals (credits) compared to option (b). The analysis shows that all accounting
options are expected to generate a surplus of credits at the EU level and, in
the absence of an explicit link to the EU's overall target or a sector target,
this option would not per se create additional incentives to increase net
removals or reduce net emissions compared to the reference scenario. In other
words, the expected impact of this option on real GHG emissions compared to the
reference scenario is zero but because of efforts that have already been
undertaken some credits would arise, depending on the accounting option chosen.
Option 2.I would mean that, for the time being, the net result of LULUCF
accounting would be a memo item in the context of the existing overall
commitment. Box 4. How have the illustrative targets been set? In Option 2.II the MS targets are based on two components; the estimated mitigation potential of LULUCF per MS at a carbon price of €5 per tCO2[30] in the different accounting options (see Annex IV for details on MSs) plus the expected credits or debits generated in the reference scenario in the different accounting options (at zero cost).The sum of these components, i.e. the target, can then be expressed as a percentage of the total GHG emissions in 1990.
5.1.1.2.
Option 2.II – Separate framework for LULUCF
(with mitigation targets)
Box 4 explains how the target was simulated
in Option 2.II. The impact on net emissions depends on the carbon price and on
the accounting rules. Figure 9 shows the real abatement potential expected at
different price levels in accounting options (b) and (c). A target based
on €5 per tCO2 would generate an increase in net removals of about 5
MtCO2 in 2020 compared to the reference scenario. Based on a target
which includes the credits generated in the reference scenario plus the
mitigation potential at €5 per tCO2 (see Box 5), this would correspond to a
target of credits equal to -0.3% in accounting option (b) and -0.8% in
accounting option (c) for the EU as a whole. The largest share of abatement
potential is expected to come from changes in forest management practices followed
by efforts to limit deforestation. Cropland management and afforestation play
only a minor role. It should be noted that the difference between the two
accounting options is small because no estimates were made of the mitigation
potential through changes in grazing land management. Also, the modelling of
mitigation measures in cropland management measures is limited. The mitigation
potential of accounting option (c) may therefore be underestimated. Figure 9 also shows the marginal cost of
abatement efforts in accounting option (a). In this case, mitigation
efforts in forest management are discounted by 85%, which means that the
accounted contribution to the overall target decreases at any given carbon
price. To reach an (accounted) reduction in the EU of 5 MtCO2, and a
target of a credits equal to -1.5% (i.e. the credits in the reference scenario
plus actual reductions of 7 MtCO2) of total emissions in 1990, a
carbon price of some €12 rather than €5/t CO2 would be required. Considering the current uncertainties
related to incomplete and uncertain emission inventories and uncertainty in the
projections (although they are partially addressed by calibrating the model
results on the basis of reported data, see Annex III for details) it is
premature to set quantitative national targets similar to ESD targets at this
stage. Further analysis would be required because the differences in the LULUCF
projections between the models and methods used are high compared to the
potential mitigation potential of around 5 MtCO2 that can be
achieved at marginal costs of up to €5 per tCO2. Fixing a
quantitative target therefore carries a risk of under- and overshooting targets
and therefore potentially of associated costs. Nevertheless, the preliminary
analysis has been carried out to inform the discussion on target setting.
5.1.1.3.
Option 3 – Inclusion of LULUCF in the ESD
In Option 3, the assumption is that MSs
keep their current targets under the ESD and that any credits or debits
generated by LULUCF count towards those targets. A redistribution of targets
has not been considered under the current 20% commitments. The carbon price is
assumed to be €5.3 per tCO2 reflecting the price expected of
transfers under the ESD to meet the national targets agreed under the Climate
& Energy Package. If the credits could be used to meet the
national targets agreed under the ESD, accounting option (a) would have
a significant impact. The credits generated in the reference scenario (Table 6)
could be as high as 90% of the cumulative emission reduction needed between
2010 and 2020 under the ESD plus the mitigation efforts at the assumed carbon
price. The accounting would therefore substantially reduce the actual emission
reduction achieved in the ESD sectors compared to 2005 and compared to the
efforts that have already been agreed in the Climate and Energy Package. For accounting options (b) and (c)
the impact would be smaller but not insignificant as the effect on the
cumulative effort between 2010 and 2020 would be between 14 to 44% of the total
required reductions in the ESD, see Table 6. The emission reduction in the ESD
would be reduced rather than simply replaced by efforts in LULUCF because all
accounting options allow for at least some degree of decrease in net removals
whilst still generating credits; this is particularly the case for forest
management under accounting option (a) for which credits are generated as long
as forest management is a sink but also, although to a smaller extent, in
accounting options (b) and (c) where no debits are awarded for forest
management as long as the sink declines according to the reference level. Figure 9. Mitigation in the EU LULUCF sector in 2020 (MtCO2
per year) Note: Effects
measures that lead to increases in the HWP pool have not been considered. Source: Böttcher et
al. (2011), based on the G4M-EUFASOM models
5.1.2.
Other environmental impacts
The impact on land use change is
expected to be minimal in the short term and at low carbon prices. In Option
2.II combined with accounting options (b) and (c), the additional
afforestation efforts and reduced deforestation are projected to have a limited
effect on the forest area (an increase by some 0.05%) in 2020 compared to the
reference scenario, and by about 0.15% in accounting option (a), see Table 6.
Land use change is expected to be zero in all other options. From a wider environmental perspective,
a reduction of soil organic matter levels because of land use or land use
change is a cause of concern not only for its link to climate change, but also
because soil organic matter is a major contributor to wide environmental
safeguards such as soil fertility as it binds nutrients to the soil, thus
ensuring their availability to plants; it is the home for soil organisms, from
bacteria to worms and insects, and allows them to transform plant residues. And
it also maintains soil structure, thereby improving water infiltration,
decreasing evaporation, increasing water holding capacity and avoiding soil
compaction. Accounting for LULUCF is therefore likely to enhance these properties.
Not including the sector in the commitments (Option 1) would discourage
the protection and/or enhancement of soil and biomass carbon stocks directly or
indirectly by not reflecting the effects of certain agricultural practices.
Even simple monitoring and reporting would create meaningful incentives to
reduce certain activities (e.g. the conversion of grassland to cropland, the
cultivation of organic soils) that are generally undesirable both from a GHG
and other environmental perspectives. Accounting option (c) would ensure
that changes in soil organic matter would be taken into account throughout the
EU whereas accounting options (a) and (b) would not as they allow MSs to
account for agricultural activities (cropland and grazing land management) on a
voluntary basis. Table 6. Environmental
impacts of the options || Option 1 || Option 2.I || Option 2.II || Option 3 || No EU action || Include LULUCF as a separate framework (No target) || Include LULUCF as a separate framework (Target = 5 MtCO2 accounted credits) || Include LULUCF in the ESD Assuming carbon price of €5 per tCO2 Accounting option || n/a || (a) || (b) || (c) || (a) || (b) || (c) || (a) || (b) || (c) MtCO2/year || 0 || 0 || 0 || 0 || -7 || -5 || -5 || 80 || 13 || 39 MtCO2 from 2013-2020 (MtCO2) || 0 || 0 || 0 || 0 || -54* || -43 || -43 || 642 || 102 || 315 % of ESD effort in 2020 || n/a || n/a || n/a || n/a || n/a || n/a || n/a || 51% || 8% || 25% % of ESD effort 2013-2020 || n/a || n/a || n/a || n/a || n/a || n/a || n/a || 90% || 14% || 44% Land use change (forest area change as compared to reference scenario, %) || 0 || 0 || 0 || 0 || 0.15% || 0.05% || 0.05% || 0 || 0 || 0 Note: Option 2.II
is based on a target of -5.4 MtCO2 accounted credits. This equals a
carbon price of €5 per tCO2
for accounting options
(b) and (c), as assumed for
Option 3, but a carbon price of €12 per tCO2 for accounting option
(a) due to the discounting of forest management. Negative values denote net removals (and positive
values net emissions). Source: Calculations based on updated version of
Böttcher et al. (2011) and JRC (2011b), reflecting the UNFCCC review, and
PRIMES-GAINS model results
5.1.3.
Possible contributions to an EU target
The possible contributions to the EU target
will differ depending on the accounting option and the context in which LULUCF
is accounted. As shown above, some credits will be generated also at a zero
carbon price. In Option 1 (No EU action) there will be no impact, and in
Option 2.I (Separate framework without mitigation targets) the impact
will be equal to that of the reference scenario which would be a memo item on
top of the 20% target. Table 7.
Possible contribution of options to the EU's GHG reduction target in 2020 for
different accounting rules (based on the reference scenario) Accounting options || Policy options Option 1 || Option 2.I || Option 2.II || Option 3 (a) Small changes to current rules || n/a || -1.4% || -1.5% || 0 (b) Outcome in the UNFCCC (I) || n/a || -0.3% || -0.3% || 0 (b) Outcome in the UNFCCC (II) || n/a || -1.5% || -1.6% || 0 (c) UNFCCC+ (I) || n/a || -0.6% || -0.8% || 0 (c) UNFCCC+ (II) || n/a || -1.9% || -1.9% || 0 Note: A minus sign
denotes credits and would contribute to reaching the EU's overall GHG reduction
target. (I) relies only model results. (II) relies on a mix of model results
and national submissions for forest management reference levels (see Table 5
for details) Source: Calculations based on Böttcher et al. (2011) and update of
JRC (2011b) Option 2.II
(Separate framework with mitigation targets) with accounting options (b) and
(c) would contribute about -0.3% to -1,6% and -0.8% to -1.9% (c)
respectively. Accounting option (a) generates a higher target (-1.4%)
due to windfall credits in the reference scenario. In Option 3
(inclusion in the ESD), part of the efforts in other sectors would be replaced
by LULUCF credits, which means that there would be no addition to the target
and, indeed, the efforts in other sectors would decrease. By way of sensitivity analysis, Table 8
shows the changes in emissions and removals resulting from a 10% change in the
EU harvest rates. A decrease (increase) in the harvest rate compared to the
reference scenario would result in credits (debits) of about 50 MtCO2
per year (0.9% of total EU GHG emission in 1990) in accounting options (b) and
(c). For accounting option (a) the accounted amount would be about 7.5 MtCO2
as a result of discounting. Table 8. Impact
on GHG emissions of changes in harvest rates Change in harvest rate || Associated change in emissions (+) and removals (-), MtCO2 || Percent of EU total GHG emissions in 1990 10% increase || 52.5 || 0.9% 10% decrease || -52.3 || -0.9% Source:
Calculations based on Böttcher et al. (2011) and JRC (2011b) Box 5.
Methodological note Forest management Importantly, the methodology used to estimate the abatement potential (and, hence, costs and impacts) of forest management imply no direct changes in harvesting rates as models are set up to keep harvest close to the reference scenario when the carbon price increases. Whilst harvest rates remain unchanged, the geography of harvest changes and forest management parameters such as rotation length and the amount of wood removed through thinning are changed for different locations of forests to secure a given level of wood supply. This shifts harvest to more expensive and less productive sites. This approach is conservative compared to approaches which would involve reducing the harvest rate (and which would instead lead to the displacement of EU production to non-EU countries). Note that this does not mean that in the reference scenario wood production is constant over time. On the contrary, wood production for biomass and other uses is expected to be around 5% higher in 2020 than in 2005 (reflected in the decrease in net removals in the reference scenario).[31] If, however, the increased costs of forest management would be reflected in increased timber prices the timber demand and harvest might however decrease slightly. Imports might increase and exports reduced (see section 5.2.2.2 for details). The effect is expected to be detectable only in Option 2.II with accounting option (a) where timber production might decrease by 0.1%. This might indirectly increase the LULUCF sink and reduce the efforts needed to meet quantitative reduction target. Cropland and grazing land management It should be noted that the mitigation potential of changes in grazing land management has not been modelled. Also, the modelling of cropland management measures is limited. The mitigation potential for agriculture may therefore be underestimated. A detailed description of the modelled mitigation measures is given in Böttcher et al. (2011).
5.2.
Economic implications
This section discusses the abatement costs
for the EU as a whole and the costs for the most affected sectors (forestry and
agriculture) and costs of monitoring and reporting. As noted in Section 2.3,
this report does not attempt to assess the impact on individual businesses such
as SMEs because legislation would involve an accounting framework at MS level
rather than provide incentives directly to different sectors or companies.
Subsequent choices on policy instruments will have to be made by MSs and the
implications cannot be predicted at this stage. However, a brief mention is
given to possible impacts by owner structure.
5.2.1.
Direct abatement costs
Abatement costs are only relevant for Options
2.II and 3 as they are the only options which would provide additional
mitigation incentives for LULUCF, see Table 9.
5.2.1.1.
Option 2.II – Separate framework with mitigation
targets
The costs of setting a target for the
LULUCF sector, based on a marginal cost of €5 per tCO2 (= 5.4 MtCO2
per year) for accounting options (b) and (c) are shown in Table 9.
The annual costs are expected to be about €27 million per year. For accounting
option (a) the costs would be higher since net removals in forest management
are discounted by 85%. This increases the marginal costs to some €12 per tCO2
to achieve the same accounted reduction of 5 MtCO2. Consequently,
annual costs would be higher and around €37 million per year. It should be
noted that the costs of accounting option (a) would be significantly higher at
higher mitigation targets. However, the results are uncertain (see Annex III)
and this will affect the ability of MSs to achieve targets as well as the costs
associated with reaching those targets. Table 9. Abatement costs per year in 2020 (€2008) || Option 2.II || Option 3 || Include LULUCF as a separate framework (Target) || Include LULUCF in the ESD Accounting option || (a) || (b) || (c) || (a) || (b) || (c) Marginal costs €/tCO2 || 12 || 5 || 5 || 0.3 || 3.8 || 1.0 Abatement costs (mln€/yr) || 40 || 27 || 27 || -166 || -55 || -156 (as % of GDP) || 0.00% || 0.00% || 0.00% || 0.00% || 0.00% || 0.00% Source: PRIMES,
GAINS and G4M model results
5.2.1.2.
Option 3 – Include LULUCF in the ESD
Meeting the targets under the ESD, whilst
assuming unlimited transfers between MSs to offset debits, is expected to
result initially in a market price of around €5 per tCO2. Under
Option 3 the price is affected in two ways. First, the accounting rules create
credits that lower the need to reduce emissions in the ESD sectors. Secondly,
the inclusion of LULUCF will create an incentive to undertake abatement also in
the LULUCF sector, if the marginal costs of abatement are lower than in the ESD
sectors. As a result, some abatement will be undertaken in the LULUCF sector.
Both effects will lower the carbon price in the ESD sectors. Table 9 shows that the impact is the
biggest in accounting option (a) because of the large amount of LULUCF
credits. As a result the price could drop to below €0.3 per tCO2.[32] Depending on the price there is an additional supply of LULUCF
reductions ranging from close to zero in accounting option (a) to around
4 MtCO2 in accounting option (b). For all options, the macroeconomic
implications will be insignificant since the changes in costs are small
compared to the expected GDP in 2020.
5.2.2.
Costs for the various sectors
Following on from Section 5.2.1, costs are
only relevant for Options 2.II and 3 as they are the only options which
would provide additional mitigation incentives for LULUCF. The two sectors that would be directly
affected by legislation are agriculture and, broadly defined, forestry. Table
10 shows the abatement costs for the sectors in 2020. The greatest contribution
to mitigation is expected to come from forestry and forest management whereas
the potential for cropland and grassland management is expected to be limited
(see Box 5). As mentioned above, the abatement costs are uncertain since the
projections are uncertain.
5.2.2.1.
Agriculture
Under Option 2.II and accounting
option (c) agriculture can contribute to some degree by improved cropland
management to enhance the sink and so faces some costs (€1 million per
year)(see Böttcher et al. (2011). i.e. pp 10-11 and p. 47 for details). Under accounting
options (a) and (b) costs would only arise if countries choose to account
for agricultural activities (which are voluntary). The effect would be most
significant for accounting option (a) since the carbon price is higher
and therefore induces higher abatement also for cropland management. However,
costs are expected to be small compared to the gross value added of the sector
in 2020. Under Option 3 the costs for
agriculture are negative. Any additional costs for cropland management are
expected to be more than compensated by the cost savings of reducing non-CO2
GHG emissions in agriculture under the ESD. If the marginal costs drop below €5
per tCO2 (expected in all accounting options) several non-CO2
abatement measures, e.g. reducing nitrous oxides from fertilizer application,
do not appear to pay off anymore. Note, however, that the marginal costs of
measures are country specific. Results might therefore differ between countries
and the analysis on the impacts per sector which was made at an aggregate EU
level. The credits and the inclusion of LULUCF options in the ESD (Option 3)
would reduce the carbon price in the ESD sector and the need to reduce
emissions in agriculture as well as other sectors. The costs for other sectors
covered by the ESD are reduced the most, especially the energy sector that
emits non-CO2 GHGs. The impact on CO2 reduction from
energy is expected to be small.
5.2.2.2.
Forestry
Impacts will differ between different
options also in the forestry sector. In Option 2.II with accounting
option (a) abatement efforts are estimated to lead to a cost increase of
0.2% relative to the production value in 2020. Elasticities of demand for
timber vary by region and product between -0.3 and -0.6 (with an average of
-0.5).[33] Subsequently, the indirect effects of passing on a cost increase of
0.2% in the timber price is estimated to lead to a reduction in demand of
around 0.1% for this particular accounting option. If international markets are
competitive relative to the EU market, part or all of the estimated decline in
demand would instead be shifted to the international market (leakage). In all
other accounting options in Option 2.II and Option 3, the
additional cost for the forest sector are much smaller in absolute terms and
relative to the value of production. Indirect effects are minimal. Table 10. Abatement costs per sector || Option 2.II || Option 3 || Include LULUCF as a separate framework (Target) || Include LULUCF in the ESD Accounting option || (a) || (b) || (c) || (a) || (b) || (c) Agriculture (€million/year) || 2* || 1* || 1 || -49 || -47 || -49 Forestry (€million /year) || 38 || 26 || 26 || 0 || 16 || 1 Other (ESD) Sectors (€million/year) || 0 || 0 || 0 || -117 || -24 || -108 SUM (€million/year) || 40 || 27 || 27 || -166 || -55 || -156 Agriculture (% of gross value added) || 0.00% || 0.00% || 0.00% || -0.03% || -0.02% || -0.03% Forestry (% of gross value added) || 0.13% || 0.09% || 0.09% || 0.00% || 0.06% || 0.00% Note: * Assumes
that MSs choose to account for cropland management where there is a mitigation
potential at €5 per tCO2.
Source:
Calculations based on Böttcher et al. (2011), using the G4M&EUFASOM, PRIMES
and GAINS. Numbers may not add up to rounding [34] Whilst Table 10 presents all forest related
impacts in terms of "forestry", the incidence is likely to be spread
over different uses. Table 11 presents costs according to the projected market
shares of energy (41%) and other wood uses (59%) in 2020. The latter group of
uses consists of sawn wood, pulp wood and other industrial roundwood. Table 11. Costs by wood use (€ million per year) Sector || Option 2.II || Option 3 Include LULUCF as a separate framework (Target) || Include LULUCF in the ESD Accounting option || (a) || (b) || (c) || (a) || (b) || (c) Energy || 15 || 11 || 11 || 0 || 7 || 0 Other wood uses || 22 || 15 || 15 || 0 || 9 || 1 Sawn wood || 13 || 9 || 9 || 0 || 5 || 0 Pulp wood || 8 || 6 || 6 || 0 || 3 || 0 Other industrial roundwood || 1 || 1 || 1 || 0 || 1 || 0 Source:
Calculations based on Böttcher et al. (2011) and Capros et al. (2010) It should be noted that the extent to which
accounting and hence the forestry sector is affected will depend on the rate of
change in the pool of harvested wood products. An increased use of wood
products would enhance the rate and generate credits which could potentially be
recycled into industry. Rüter (2011) calculated changes in this pool for the
years leading up to 2020 based on projections of harvest rates provided by
Böttcher et al. (2011) and various country-specific models.[35] The current increase is expected to continue. As shown in Table 12,
with a net pool increase estimated at approximately 52 MtCO2 in
2020. However, emissions / removals fluctuate between single years and it may
therefore be more appropriate to refer to the average of 2013-2020 of 55 MtCO2.
The HWP pool can increase in the short to medium term and may stabilise in the
long term when removals from new wood products can compensate emissions from
old wood products (i.e. inflow equals outflow). Table 12
Historic (up to 2009) and projected net emissions from the HWP pool (MtCO2)*,
** || Year || Average || 2000 || 2005 || 2010 || 2015 || 2020 || 2013-20 EU total || -52.9 || -59.6 || -64.3 || -56.8 || -52.0 || -54.9 Note: * Negative
values denote a pool increase. ** The calculations are subject to change
following the UNFCCC review process of reference levels (incl. HWP) as
submitted by MSs. Source: Rüter
(2011) Another important factor to consider is the
potential benefits in terms of GHG savings that can be achieved as a result of substitution
of materials and / or energy sources for biomass that will appear in the
accounting of other (non-LULUCF) sectors. These emission savings would be accurately
reflected in accounting only once the related emissions are recorded in LULUCF.
In a review of some 20 studies, Sathre and O'Connor (2010) found that the
amount of carbon emissions avoided by the use of wood instead of non-wood
material averaged at a factor of two. In other words, for each tC in wood
products that substitute non-wood products, an average GHG emission reduction
of 2.1 tC can be expected. The primary reason is that wood products generally
require less total energy (in particular fossil fuel consumption) in
manufacturing compared to products made from alternative materials. The energy
recovery from biomass residues associated with wood products, e.g. wood
processing residues, and the use of post-use wood products for energy purposes
are also important.
5.2.2.3.
Impacts by owner structure
Any positive direct costs of accounting for
LULUCF (Options 2.II and 3) will mainly be borne by the forest sector.
Some 46% of the forest is owned by individuals and families, some 11% is owned
by forest industries and private institutions whereas local communities own 2%
and the remaining 41% is public ownership (UNECE, 2011). Roughly half of any
additional costs incurred for the forest sector (see Table 10) could therefore
be borne by individual / family forest owners and 10% by private business
entities and institutions (including SMEs and micro-firms), and another 40% by
the public sector. Estimates suggest that the number of private forest owners
might be around 15 million[36]. The most costly Option 2.II with accounting option (a)
would involve an average additional cost per (private) forest owner of around
€2 per year. Accounting options (b) and (c) would imply average costs of
only some €1 per forest owner per year.
5.2.3.
Monitoring and reporting costs
The implications of changes in monitoring
and reporting requirements need to be assessed in two ways; some impacts stem
from the administrative burden of changes in the scope of mandatory accounting
and the accounting methods applied, and others relate to the additional efforts
required by MSs to reach the objective defined in this impact assessment of
reporting according to the IPCC GPG. The costs are the same for Options 2 and
3, although they vary depending on accounting option, and zero for Option 1.
5.2.3.1.
Administrative burden of changing the scope and
methods of accounting
Some reporting costs would arise as a
result of changing the scope and modalities of accounting because the data
needs for reporting on land use activities under the KP go beyond those for
reporting on land use categories under the UNFCCC. The associated costs largely
form part of the impact assessment of revising the Monitoring and Mechanism
Decision (208/2004/EC) but are included also here for reasons of transparency,
see Table 13. No additional reporting requirements would
be implemented for afforestation, reforestation and deforestation. Today,
accounting for forest management is not mandatory, but 17 MSs have already
chosen to account for associated emissions and removals. Making this a
mandatory activity would involve additional efforts for 10 MSs at a total cost
of about €0.4 million per year. Necessary updates of the data on forest
management required for accounting options (b) and (c) is expected to be
negligible (a total €20 thousand per year) and the mandatory reporting of
changes in the HWP pool is estimated to cost roughly €0.3 million per year.
There will be additional costs from the introduction of mandatory accounting
and reporting of cropland and grazing land management activities in accounting
option (c) at approximately €0.7 million per year. Following the adoption of
the 2006 IPCC GPG costs could become higher but are not estimated here. Table 13.
Administrative burden of additional mandatory reporting requirements (€ million
per year for the EU-27) Reporting item || Accounting option (a) Small changes || Accounting option (b)* Likely outcome in the UNFCCC negotiations || Accounting option (c)* UNFCCC+ Report emissions and removals from forest management activities on a mandatory basis || 0.35 || 0.35 || 0.35 Report data on changes in the harvested wood products pool || - || 0.30 || 0.30 Updating of forest management reference levels || - || 0.00 || 0.00 Reporting emissions and removals for cropland and grazing land management || - || - || 0.70* EU total || 0.35 || 0.65 || 1.35 Note: The cost of
reporting items equal to accounting option (a) have been addressed in the
impact assessment of the proposal for the revised Monitoring and Mechanism
Decision (208/2004/EC) and related Commission Decision (2005/166/EC) and are
included here for reasons of transparency. Note that an international agreement
is likely generate the same additional administrative burden as accounting
option (b). * This estimate is an approximation based on the estimated costs
associated with reporting for rewetting and draining on land with organic
soils. Source: European Commission impact assessment of
the proposed revision of the Monitoring
and Mechanism Decision (208/2004/EC)
5.2.3.2.
Implications of improved monitoring and
reporting
Whilst additional efforts will be required
by MSs to improve the various aspects of monitoring and reporting, it must be
noted that the related costs would not be the result of including LULUCF in the
EU's GHG emission reduction commitments. MSs are already required to use higher
tiers for key categories, corresponding to Steps 1 and 2 of improving
monitoring and reporting as outlined in Section 4.3 (as a result of existing
reporting obligations and commitments under the UNFCCC and the KP). However,
since improvements will generally be required by MSs, estimates of the costs
for improving the reporting on soils are presented here and in Annex I by way
of illustration. In tems of the first step, reporting on
biomass in forest land is generally complete today, but many MSs do not report
on soil in one or two of the sub-categories under forest land, cropland and
grassland. Applying tier 1 requires compiling activity data (i.e. area
disaggregated by broad climate and soil type and by broad management regime)
from different sources in MSs and / or from European initiatives such as LUCAS[37] and CORINE for recent years, to be combined with IPCC default
values for carbon stock and stock changes. Some MS still have difficulties in
tracking the history of land over time in order to calculate land conversion.
While the effort needed to improve the completeness for mineral soils can be
assumed to be reasonable, it must be understood that tier 1 reporting provides
limited information about the effects of mitigation measures. On organic soils,
and for land conversion, higher tiers need to be applied. However, as organic
soils only make up 2% of the total surface, the additional efforts should be
limited. This first essential step could be taken rather swiftly. As regards the second step, MSs such as
Sweden, the UK, Denmark, Finland and Germany, already apply high level tiers to
the soil pool in all land categories, but most MSs use tier 1 or do not report
at all on soil in at least one of the land categories. There is also a need for
improved reporting on dead organic matter in forest land, although the
significance varies and, in cropland and grassland, simpler methods are
acceptable also under tier 2 (IPCC, 2003). Country-specific data are generally
available in the EU-15, but less so for the EU-12. Finally, additional efforts
may be needed for measuring stock changes in perennial vegetation in cropland
and grassland. Data needed to calculate the soil emissions factors could be
collected e.g. through repeated soil sampling campaigns in order to capture the
annual rates of emissions and removals. Substantial work has already been
carried out for the proposal of a European Soil Framework Directive and shows
that there is capacity to build on (see e.g. Kibblewhite et al., 2008; Hiederer
et al., 2011, EC, 2003). Activity data would have to be collected making use of
existing data sources (IACS, FSS, FADN, crop statistics). More work is required
to integrate existing data on soil types (e.g. EU soil map, LUCAS, national
land surveys) with activity data on field management. Annex I provides estimates of soil sampling
costs resulting from the calculation of country-specific emission factors for
soil, equivalent to Steps 1 and 2. However, the required frequency and scope of
soil sampling will vary across MSs. If, for the purpose of GHG inventories,
emission factors are developed through targeted research and combined with
modelling tools, soil sampling will play the role of verifying, improving and
calibrating the estimated data. Other methods, including eddy covariance
measurements (used e.g. in CarboEurope) and chronosequencing[38] can contribute in providing the necessary inputs. If all MSs require investments in soil
monitoring to move to country specific data in reporting, the annual cost for
the EU-27 are expected to amount to between €0.5-2.1 million per year using a
sampling interval of eight years (which corresponds to the commitment period of
2013-20). However, and as shown in Table A1.10 in Annex I, a number of MSs
already use country-specific data which means that the cost is likely to be
lower, between €0.4-0.5 million per year at the EU level. Arrouays et al. (2008) estimated the number
of additional sampling sites needed to detect a relative decrease of 5% in soil
organic carbon. If the same cost assumptions are applied to their estimate, the
resulting costs would be €0.9-1.2 million per year, i.e. about double the costs
compared to the most conservative baseline assumption. On the other hand, a
soil sampling campaign was carried out under LUCAS[39] in
2009 at an additional (soil-sampling specific) cost of only €1.2 million (or
€0.2 million per year) which is much lower than any of the estimates given
above. This shows that there are significant potential cost savings through the
use of coordinated and systematic schemes. Although costs will vary between MSs
and with the methods applied, the above estimates provide a cost interval with
an average of €1.1 million per year of the additional investments required in
MSs. The costs also depend vey nuch on the parameteres assessed and the depth
of soil sampling. LUCAS was restricted to 0-30 cm and a reduced number of
parameters. For example soil bulk density was not included which accounts for
as sizeable part of the costs of soil survey. However, the parameters assessed
may be sufficient for the purpose since change in soil carbon may only occur in
the layer addressed for the time frame. On top of meeting the IPCC GPG
standards, additional efforts to harmonise monitoring and reporting in the EU
should be promoted (Step 3). It has not been possible to estimate the
associated costs at this stage.
5.3.
Social implications
In this section the social implications of
Options 2.II and 3 are examined in terms of impacts on employment and the
distribution of costs and cost savings (or revenues) over the MSs. Option 2.I
is formulated such that the results of accounting are treated as a memo item to
the EU's current overall GHG reduction target and there are therefore no
distributional costs of this option. Similarly, costs are zero for Option 1.
5.3.1.
Costs by Member States
5.3.1.1.
Option 2.II – Separate framework with mitigation
targets
In Option 2.II the distribution of
cost per MS depends on the estimated mitigation potential. The target for each
country is set on the assumption that all countries would face the same
marginal costs of €5 per tCO2. This is then translated into an
amount to be reduced by each country over and above the credits generated in
the reference scenario. The total amount to be reduced is some 5 MtCO2
at the EU level on top of the credits generated in the reference scenario. It
should be noted that uncertainties on the expected costs per MS are larger than
uncertainties at the EU level. Under accounting option (a)
reductions from forest management are discounted by 85%. This means that more
real reductions (around 14 Mt) are needed to meet the accounting target of
around 5 MtCO2 and this generates a higher carbon price of around
€12 per tCO2. Consequently, overall costs for the EU (€40 million
per year in 2020) and for MSs are higher here than in the other accounting
options. The illustrative targets are based on the mitigation potential at a
given carbon price. The largest absolute costs would fall on the following MSs:
Finland, Bulgaria, Rumania, Estonia, France and Italy (see Table A4.6 in Annex
IV). Under accounting options (b) and (c) the total cost for the EU
would be smaller at about €27 million in 2020. Most of the costs would fall on
Finland, Bulgaria, Estonia, France and Italy (see Table A4.6).
5.3.1.2.
Option 3 – LULUCF included in the ESD
In Option 3 all MSs benefit from a
reduction in the cost of meeting the ESD targets but some MSs benefit more than
others. This is because there are net credits at an EU level that reduce the
need to cut emissions for sectors covered by the ESD. In this case it is
assumed that the price of ESD transfers is not fixed at €5.3 per tCO2
(as expected to meet the agreed ESD targets) but is lowered for two reasons:
the net LULUCF credits at EU level that lower total demand for emission
reductions and the marginal costs and the additional supply of LULUCF sink
enhancement measures induced by a positive carbon price in that sector. This is
particularly relevant for accounting option (a) where credits are high
(50% of the emission reduction needed in the ESD sectors in 2020, and 90% of
the accumulated emission reduction over the period 2013-20) and the ESD price
is reduced to less than €0.3 per tCO2 in 2020 in the ESD sector.
Total cost savings are estimated at €166 million, see Table A4.6 in Annex IV
for MS results. Note that some MSs face additional costs since they have net
debits rather than credits. Under accounting option (c) the net LULUCF
credits are limited compared to the total reduction required under the ESD
(around 10% of the reduction effort in 2020, and 15% of the accumulated
emission reduction) so the impact on the carbon price and MSs abatement efforts
is smaller. The costs savings of accounting option (b) are in between
options (a) and (c).
5.3.2.
Employment
Since the costs of the policy options are
negligible in terms of share of GDP (see Table 9) the overall impacts on
employment will be very small as well. Under Option 2.II combined with accounting
option (a) costs are the highest and this is expected to lead to a loss of
timber production in the EU in the forest sector of around 0.1%. For the two
other accounting options output changes are even smaller at about
0.01-0.02%. Calculations (based on Eurostat (2009; p.37) show that Option
2.II combined with accounting option (a) could lead to a direct loss of
some 175 (full-time) jobs in forestry. For accounting options (b) and (c)
direct jobs losses are estimated at around 125 jobs. However, any job losses will also be
(partially) compensated by the additional demand for labour related to changes
in forest management (e.g. increased thinning) to enhance the carbon sink.
Based on the share of labour costs in forest management costs and the labour
costs per person the number of jobs created in forest management could be
around 210 for Option 2.II combined with accounting option (a)
and around 180 for accounting options (b) and (c). Whilst uncertainties
are big, the assessment suggests that the overall impact on employment are very
small and may be positive or neutral. For Option 3 there could be job
increases since costs are reduced in agriculture and other ESD sectors. Based
on the analysis carried of options to move beyond 20% greenhouse reductions one
can estimate the possible impacts.[40]
Stepping up to 25% would entail additional cost of €33 billion and could create
160000 jobs or reduce 160000 jobs depending on how tax revenues would be used.
Option 3 would reduce these costs by respectively 166 million in accounting
(a), 55 million (option b) and 156 million (option c), see
Table 9. Depending on how tax revenues changes are used the number of (net)
jobs by around 800, 760 or by around 270 jobs respectively. The impacts are
hence very small.
6.
Comparing the options
This section provides a comparison of the
extent to which the different options answer to the problem definition and
operational objectives. The impact assessment shows how accounting for LULUCF
can enhance the environmental integrity, policy coherence and economic
efficiency of the EU's GHG emission reduction commitments, as defined in the
problem definition.
6.1.1.
Choosing the right policy context
The analysis shows that only two broad
options would meet the overall objective set out by the European Council and
Parliament that all sectors should contribute to the EU's overall GHG
reduction commitment; namely the inclusion of LULUCF in the EU's GHG
reduction commitment via a separate framework (Option 2) or though the
ESD (Option 3). The environmental, economic and social impacts of the
options differ largely depending on the accounting rules applied, as shown in
Section 5. The objective to limit the impact of the
high inter-annual variability of emissions and removals in LULUCF and their
inherent reversibility on compliance poses an important challenge for the
inclusion in the policy frameworks that currently regulate the EU's reduction
targets. The ESD (Option 3) is based on annual compliance and requires
MSs to decrease (or limit increases in) emissions according to a linear
trajectory. This is a key feature to ensure that the targets are met in 2020
and to limit the cumulative emissions in the commitment period. However, there
is a significant compliance risk associated with applying this approach to
LULUCF. Annual compliance would be difficult to apply where, over the period
1990 to 2008, many MSs experienced variability in net emission between two
adjacent years of around 20% (Sweden, Austria and Portugal), 40% (Finland), 50%
(Latvia) or 60% (Estonia) compared to their total ESD emissions in 2008. This
and the frequent and significant recalculations of reported data clearly pose a
challenge for annual compliance with a linear trajectory and would in many
cases greatly exceed the flexibilities of the ESD. In addition, the long
lead-time of many measures in LULUCF means that annual accounting is not as
meaningful as in other sectors, and a linear trajectory with required emission
reductions each year will generally not be relevant. Option 2 (a
separate framework) would be able to address these issues by averaging
emissions and removals over the commitment period and therefore meet the
objective related to inter-annual variability. Another issue to note associated
with Option 3, particularly as long as the EU's emission reduction
target remains at 20%, is that the inclusion of LULUCF would reduce the agreed
efforts for the sectors that are already part of the existing commitments and
so effectively reduce the EU's commitment. Option 2 would avoid this
issue.Ensuring robust accounting. The objectives of accounting are to ensure
an extensive coverage of emissions and removals, thereby providing a level
playing field between mitigation options, and to ensure that non-permanence is
reflected in accounting and to prevent large natural disturbances from
negatively affecting the compliance risk of MSs. Three options were considered
to meet the objectives: (a) small changes to the rules under the KP's first
commitment period, (b) rules which correspond to the expected outcome in the
UNFCCC negotiations, and (c) rules which correspond to the expected outcome of
the UNFCCC but with an improved scope of the emissions and removals accounted
for. Table 14 provides a summary assessment of the extent to which the
different accounting options meet the objectives. Table 14.
Performance of the different accounting options Objectives || Extent to which the objectives are met by the different accounting options Accounting option (a) Small changes || Accounting option (b) Likely outcome in the UNFCCC negotiations || Accounting option (c) UNFCCC+ Provide a level playing field between different mitigation options || X || ● || ●● Ensure extensive coverage of emissions and removals || ● || ● || ●● Ensure that non-permanence is reflected in accounting || X || ● || ●● Prevent large natural disturbances from negatively affecting the compliance risk of MSs || ● || ●● || ●● Notation key: x
Objective not or insufficiently addressed by option, ● Objective
partially addressed by option, ●● Objective sufficiently addressed
by option As regards providing a level playing
field between different mitigation options, the most important activity is
forest management. This is treated differently in the different accounting
options. Table 5 shows that accounting option (a) generates substantial
credits, about 80 MtCO2 per year between 2013 and 2020. These are
largely a "windfall" (free) as they include removals that would have
occurred without any change in management activities. Any mitigation efforts
will be discounted by 85% and this will make mitigation very expensive and so
limit incentives for additional mitigation efforts. It would also allow for
substantial decreases in net removals and increases in net emissions without
any real economic impacts. Finally, it would not meet the objective of ensuring
that non-permanence is reflected in accounting because no emissions and
removals related to agricultural activities, and only a fraction of those
related to forest management, will be accounted. Accounting options (b) and (c) allow for a change in the sink due to natural saturation and
existing policies without generating debits or credits. They do so to factor
out changes in emissions and removals that are not human-induced. However, for
forest management they require full accounting for any deviations from the
"reference level". In practice, this means that in the reference
scenario all abatement options and uses, whether sequestration or additional
use of biomass for energy production (e.g. for reaching the RES-D targets) or
material substitution, will face the same opportunity cost and this will ensure
a level playing field between different mitigation options. In line with
the agreement in Durban is the cap on credits from forest managment. An
additional dimension to accounting for forest management is to put a
quantitative limitation ("cap") on the emissions and removals
accounted. The cap limits the undue benefits (credits) related to the
uncertainty associated with the projections which underpin the "reference
levels". Annex II describes the impact on accounting of the cap on credits
of 3.5% agreed in Durban. In terms of ensuring extensive coverage
of emissions and removals in accounting and that non-permanence
is reflected in accounting, only accounting option (c) requires MSs
to account for emissions and removals in both agriculture and forestry on a
mandatory basis whereas both accounting options (a) and (b) allow accounting
for agriculture to be voluntary. Voluntary accounting may put the credibility
of the EU's commitment at risk as the choice of which activity to account for
can be perceived as opportunistic. A wider scope of accounting would increase
the consistency between MSs as some account for these activities under the
first commitment period of the KP and others not. It is important that all
sectors in all MSs are recognised as contributing to reaching the targets of
the "Europe 2020" strategy, both to secure a level playing field for
business and MSs and a fair distribution of efforts, and to ensure a consistent
treatment of agriculture, forestry and industry within the EU's internal
market, i.e. to provide a level playing field. In the longer term, a
more inclusive accounting would also be conducive to increasing the
cost-efficiency in reaching any given overall target. As regards the objective of reducing the
impact of natural disturbances on compliance risk, accounting options
(b) and (c) would include provisions for accounting for large natural
disturbances and so limit the risk of non-compliance with GHG reduction targets
if emissions occur as a result of large natural disturbances which are beyond
the control of MSs. Annex II provides more details on the effects on accounting
of different thresholds for excluding emissions from accounting. However, the
application of the provisions is likely to be limited to a handful of MSs and
to have only a small impact on accounting for the EU as a whole. If the
emissions beyond 5% of the total emissions of a MS in 1990 were to be used the
impact on the EU's overall accounting would be negligible whilst at the same
time provide the necessary safeguards for those MSs most affected (in
particular Portugal).
6.1.2.
Improving monitoring and reporting
This impact assessment outlines a
three-step approach to meet the objective of ensuring that monitoring and
reporting comply with IPCC methodological guidance. A first step would
involve achieving complete reporting using at least simple methodologies. This
first essential step could be taken rather swiftly. A second step would mean
increasing the accuracy of the reported data using more sophisticated methods.
Essential progress is expected during the first commitment period of the KP but
efforts will have to continue also during the commitment period 2013-20.
Lastly, further improvements of the comparability between MSs can be achieved by
harmonising monitoring, reporting and related nomenclature. Although important
steps towards harmonisation have already been taken and is part of compliance
with the IPCC guidelines (steps 1 and 2), further efforts will be required over
the next commitment period. The above steps would essentially form part
of the Commission's proposal for a revised Monitoring Mechanism Decision, see
separate proposal and impact assessment, but are included here for
illustrational purposes. It should be noted also that MSs are generally obliged
to take the first two steps as part of their commitments under the UNFCCC and
the KP and the cost of introducing the requirements in EU legislation are
therefore close to zero. This said, further improvements are generally needed in
MSs but could be achieved at low costs. Changes in the scope and methods of
accounting would however generate additional but small administrative /
reporting costs, estimated at approximately €0.4 million to €1.4 million per
year for the EU as a whole, with the more extensive accounting option (c)
at the upper end of the interval.
6.2.
Concluding comments
There were two important reasons for
initially leaving LULUCF out of the EU's climate change commitments. Firstly,
the deficiencies of existing accounting rules under the KP needed to be
addressed. Secondly, the expectation was that the Copenhagen world climate
summit in 2009 would deliver an international agreement on climate change,
including revised accounting rules for LULUCF which could then be adopted by the
EU. This did not happen, and despite the progress achieved through the
Copenhagen Accord and the Cancun Agreements there is still not an agreement in
place. It is clear that the views of both developed and
developing countries are converging and there is widespread support for the
EU's favoured options on the
key remaining issues. But LULUCF is only one part of wider and very complex
discussions. As a result, an accounting framework for LULUCF risks being
further delayed. There are good
reasons to include LULUCF in the EU's GHG emission reduction commitments to
improve the policy coherence, environmental integrity and economic efficiency
of the EU's GHG emission reduction commitment. But the inclusion requires that
the special features of LULUCF and circumstances of MSs are suitably addressed.
It is therefore important to ensure that robust accounting rules and monitoring
and reporting are in place. In terms of accounting,
accounting option (c) involves inclusive accounting of emissions and
removals from both forestry and agricultural activities and gives equal weight
to mitigation efforts whether undertaken in the forestry, agriculture, industry
or energy sectors. This is conducive to cost-efficiency and will ensure a level
playing field for both MSs and the different sectors of the EU's internal
market. It will also provide a framework in which mitigation efforts by farmers
and foresters and industry are incentivised, visible and correctly reflected. A
wide coverage of emissions and removal will also ensure that potential
reversals are reflected in accounting. Monitoring
and reporting needs to be improved in support of
the accounting framework and of indicators for progress in agriculture and
forestry. The Commission proposes to achieve this through separate legislation,
i.e. a revision of the Monitoring Mechanism Decision. For reasons of both
comparability and cost-efficiency, better use should be made of EU wide
monitoring instruments such as LUCAS and CORINE. Robust
accounting and monitoring are important steps, but not sufficient. For strong
incentives to be provided, the results of efforts by sectors need to count
towards the EU's GHG emission reduction commitments. This will only be possible
if the right policy context for LULUCF is established. The high
variability of emissions and removals in forests means that annual emissions
reduction targets that apply to other sectors are unsuitable. The long lead
times needed for mitigation measures to take effect also set LULUCF apart from
most other sectors. The results of this impact assessment suggest that a
separate framework would be best suited to address the special circumstances of
LULUCF. However, the EU has already committed to
reducing greenhouse gas emissions by 20% by 2020 compared to 1990 through efforts
in other sectors and is on track to reach that commitment without the
contribution of LULUCF. Before the level of ambition is increased beyond 20%
conditions need to be right. The sector should therefore be formally included
in the target only once the EU decides to increase the level of ambition (Option
2.I). This does not mean that mitigation action should be put on hold.
National action plans could be prepared to provide a strategy and outlook for
LULUCF as well as an intermediate step towards the full inclusion of the sector
and its integration with current policies.
7.
Monitoring and evaluation
Reporting will be reviewed and monitoring
will be carried out as part of the existing requirements and arrangements under
the UNFCCC and successor to the KP in terms of: · Completeness, accuracy and consistency of monitoring and reporting
(in line with the relevant IPCC methodological guidance), · The correct application of accounting rules As appropriate, monitoring and evaluation
will also be ensured through the proposed amendments of the Monitoring
Mechanism Decision (280/2004/EC) and related Commission Decision (2005/166/EC).
8.
ANNEXES
8.1.
Annex I – Monitoring, reporting and verification
With the exception of Section 8.2.8, this Annex
has been authored by the Joint Research Centre and is referred to in the main
text as JRC (2011a).
8.1.1.
Introduction
The following text presents an overview of
the status of monitoring, reporting and verification of GHGs in LULUCF in the
EU-27,[41]
By monitoring is meant activities that are carried out to estimate
emissions and removals, by reporting the inclusion of information
collected during the monitoring phase in the national annual GHG inventory[42], and by verification
the comparison of the emissions/removals estimates reported in the GHG inventory
with estimates derived from independent assessments. The assessment focuses on
four aspects of monitoring and reporting, namely completeness of land
categories/activities and carbon pools reported, accuracy of estimates, consistency
of estimates over time and comparability of estimates among MSs. A brief
discussion on existing verification activities is also provided. A more
detailed compilation of data from the UNFCCC and KP reporting can be found in
the annual European Union greenhouse gas inventory 1990 – 2008 and inventory
report 2010 (http://www.eea.europa.eu/publications) and
in the JRC LULUCF tool (http://afoludata.jrc.ec.europa.eu/index.php/models/detail/7).
8.1.2.
Completeness
8.1.2.1.
Convention reporting
Reporting under the convention is mandatory
for the land categories for which IPCC GPG exists, i.e. forest land, cropland
and grassland, and all land use changes (for all land categories). According to
IPCC GPG-LULUCF, emissions and removals from key categories[43] should be estimated with
higher-tiers methods (i.e. tier 2 or 3) [44].
Table A1.1 gives a detailed account of the reporting requirements associated
with land use categories, either with tier 1 or tier 2 methods. It should,
however, be noted that Cyprus and Malta are not part of Annex I to the
Convention and therefore not obliged to report in the same way. It is therefore
relevant to discuss the 25 MSs that are obliged to report. Table A1.2 shows reported emissions (E) and
removals (R) as well as the key categories (shaded cells) for the three main
UNFCCC land categories (“wetlands”, "settlements" and
"other" have been omitted). Reporting by the 25 MS obliged to report
under the UNFCCC for the various land and sub categories shows complete
coverage for forest remaining forest and nearly complete coverage (24 MS) for
land converted to forest. Coverage is reasonably good also for cropland (22 MS
reported cropland remaining cropland and 18 MS land converted to cropland) and
grassland (corresponding numbers are 18 and 22). Whilst the coverage of
categories was significantly improved in 2008, especially for land use changes,
likely due to the first reporting under the KP, there are still some gaps in
the reporting of mandatory categories. Table A1.3 shows reported emissions (E) and
removals (R) by carbon pools[45].
Note that in some cases the assumption of no C stock change is allowed by IPCC [46]. Reporting for biomass is by
far the most complete across land categories, in particular for forest land,
and less so for soil and dead organic matter. However, for grasslands soils are
more important and this is reflected in reporting. Most of the EU-12 reported
less sub-categories and pools than the EU-15 MS. This difference is not only
because of lack of national data, but also because of lack of resources for
processing the existing data (e.g. available from national statistics) and to
adapt and develop it according to the UNFCCC/IPCC reporting needs. Table A1.1
Mandatory categories and carbon pools to be reported under the Convention,
using tier 1 or tier 2 methods (tier 2 is mandatory if the category is “key
category”) || TIER 1 || Forest land || Cropland || Grassland || Wetlands || Settlement || Other land || FLrFL || LcFL || CLrCL || LcCL || GLrGL || LcGL || WLrWL || LcWL || SLrSL || LcSL || OLrOL || LcOL TIER 1 || LB || AB || Y || Y || Y 1 || Y 2 || || Y 2 || || Y 2 || || Y 2 || || Y 2 BB || || Y || || Y 2 || || Y 2 || || Y 2 || || Y 2 || || Y 2 DOM || DW || || || || X 2 || || X 2 || || || || || || L || || || || X 2 || || X 2 || || || || || || SOM || mineral || || Y || Y || Y || Y || Y || || Y || || || || Y 3 organic || Y || Y || Y || Y || Y || Y || || Y || || CO2 || liming || || || Y || Y || Y || Y || || || || || || N2O || fertilization || || || T 4 || 4 || 4 || 4 || || || || || || conversion || || || || Y || || Y || || || || || || drainage || Y || Y || 4 || 4 || 4 || 4 || || || || || || fires || Y || Y || 4 || 4 || Y 5 || Y 5 || || || || || || CH4 || fires || Y || Y || 4 || 4 || Y 5 || Y 5 || || || || || || TIER 2 || LB || AB || Y || Y || Y 1 || Y || Y || Y || || Y || || Y || || Y BB || Y || Y || || Y || Y || Y || || Y || || Y || || Y DOM || DW || Y || Y || || X 2 || || X 2 || || || || || || L || Y || Y || || X 2 || || X 2 || || || || || || SOM || mineral || Y || Y || Y || Y || Y || Y || || Y || || || || Y organic || Y || Y || Y || Y || Y || Y || || Y || || CO2 || liming || || || Y || Y || Y || Y || || || || || || N2O || fertilization || Y || Y || 4 || 4 || 4 || 4 || || || || || || conversion || || || || Y || || Y || || || || || || drainage || || || 4 || 4 || 4 || 4 || || || || || || fires || Y || Y || 4 || 4 || Y 5 || Y 5 || || || || || || CH4 || fires || Y || Y || 4 || 4 || Y 5 || Y 5 || || || || || || Notation keys: FLrFL: forest land remain forest land;
LcFL: land converted to forest land (and so on for the other categories) LB Living biomass (AB: Above
ground biomass; BB: Below ground biomass) DOM Dead organic matter (DW: Dead
wood; L: Litter) SOM Soil organic matter Y Mandatory X To be mandatorily
reported only in the conversion year (from forest) blank Non mandatory (or IPCC
assumes no change in carbon stock) 1 Perennial woody crops,
only 2 To be reported as
instantaneously oxidized in the year of conversion 3 To be reported fully
oxidized in conversion 4 To be reported under
the agriculture sector 5 Savanna burning shall
be reported under the agriculture sector Table A1.2.
Completeness of reporting in each MS by land use categories (data for the year
2008, from the GHG inventory 2010). In each land use category, first column
(e.g. F-F) identifies a land remaining in the same category, while second
column (e.g. L-F) identifies a land converted to a new category. Party || Reporting category Forest land || Cropland || Grassland 5.A.1. || 5.A.2. || 5.B.1. || 5.B.2. || 5.C.1. || 5.C.2. FLrFL || LcFL || CLrCL || LcCL || GLrGL || LcGL Austria || R || R || R || E || R || E Belgium || R || R || E || E || E || E Denmark || E || E || E || R || E || E Finland || R || E || E || E || E || R France || R || R || || R || || R Germany || R || R || E || R || E || R Greece || R || R || R || || || Ireland || R || E || R || E || E || R Italy || R || R || R || || R || R Luxembourg || R || R || E || E || || E Netherlands || R || R || || E || E || E Portugal || R || R || R || E || || R Spain || R || R || R || || || R Sweden || R || R || E || E || R || R UK || R || R || E || E || E || R Bulgaria || R || R || E || E || || R Cyprus || R || || || || || Czech Rep. || R || R || R || E || R || R Estonia || R || R || E || E || R || R Hungary || R || R || R || E || E || R Latvia || R || R || E || E || E || Lithuania || R || R || || || || Malta || R || || R || || || Poland || R || R || E || || E || R Romania || R || || || || || Slovakia || R || R || R || E || || R Slovenia || R || R || E || E || E || E Notation key: R=
Removals, E = Emissions. The first column of each category denotes land
remaining in the same land category whereas the second column denotes land
converted to that land category. Shaded area indicates that the category is a
key category. Note that Cyprus and Malta do not have the same reporting
obligations under the UNFCCC. Table A1.3.
Completeness of reporting in each MS by pools for main land use categories
(data for the year 2008, from the GHG inventory 2010). For transparency, the
soil pool is separated into mineral and organic soil. || Forest land || Cropland || Grassland || FL-FL || L-FL || CL-CL || L-CL || GL-GL || L-GL || Biomass || DOM || SOM Min || SOM Org || Biomass || DOM || SOM Min || SOM Org || Biomass || DOM || SOM Min || SOM Org || Biomass || DOM || SOM Min || SOM Org || Biomass || DOM || SOM Min || SOM Org || Biomass || DOM || SOM Min || SOM Org AT || R || R || || || R || R || R || || E || || R || || R || E || E || || || || R || ie || E || E || E || BE || R || R || R || || R || || R || || || || E || || E || || E || || || || E || || E || || E || DK || R || E || || E || E || R || E || E || E || || R || E || R || E || R || ie || E || || E || E || E || E || R || E FI || R || ie || R || E || R || ie || E || E || R || || R || E || R || R || E || E || || || R || E || R || R || R || E FR || R || R || || || R || R || R || || || || || || R || R || R || || || || || || E || E || R || DE || R || R || || E || R || R || R || E || R || ie || R || E || R || R || R || E || R || ie || || E || R || ie || R || E GR || R || || || || R || || || || R || || R || E || ie || || || || || || || || || || || IE || R || R || || R || E || R || E || R || || || R || || || || E || || || || || E || R || || R || E IT || R || R || R || || R || R || R || || R || R || R || E || || || || || R || R || R || || E || || R || LU || R || || || || R || || R || || E || || || || E || E || E || || || || || || E || E || R || NL || R || R || || || R || || || || || || || ie || E || E || || || || || || E || E || E || || PT || R || E || R || || R || E || R || || R || E || E || || E || E || E || || || || || || E || E || R || ES || R || || || || R || || R || || R || || R || || || || || || || || || || || || R || SE || R || R || R || E || R || R || R || E || R || E || E || E || E || E || E || E || R || R || R || E || R || E || R || E UK || R || R || R || R || R || R || R || R || R || || Ie || E || E || ie || E || ie || || || || || E || ie || R || ie BG || R || || || || R || || E || || E || ie || R || || R || || E || || || || || || E || || R || CY || R || || || || || || || || || || || || || || || || || || || || || || || CZ || R || || || || R || || R || || R || || R || || E || E || E || || || || R || || E || E || R || EE || R || R || || E || R || ie || || E || || || || E || || || || E || R || E || || E || R || || || E HU || R || || || || R || || || || E || || R || || R || ie || E || || || || E || || R || ie || R || LV || R || || || E || R || || || E || || || || E || ie || E || || ie || || || || E || || || || ie LT || R || R || R || E || R || || || || || || || || || || || || || || || || || || || MT || R || || || || || || || || R || || || || || || || || || || || || || || || PL || R || || R || || R || R || R || || R || R || E || E || || || || || || R || || E || || R || || RO || R || || || || || || || || || || || || || || || || || || || || || || || SK || R || || || || R || || R || || R || || || || E || E || E || || || || || || R || E || R || SI || R || R || || || R || R || R || || R || || E || E || E || || E || || || || || E || E || || R || R, E - carbon pools changes result in either Removal or
Emissions; "ie" means "included elsewhere"; empty cells
show either "not estimated" (reported in CRF as "NE" alone
or in combination with other keys), assumed as "no C stock change"
(following IPCC tier 1), or assumed as "not occuring" (notation keys
used "NO" and/or "NA")
8.1.2.2.
Kyoto Protocol reporting
Table A1.4 shows activities accounted for
by MSs and key categories for the EU-25. Reporting and accounting of LULUCF
eligible activitites are mandatory for afforestation and reforestation (AR) and
deforestation (D) under Art 3.3, and optional for forest management (FM),
cropland management (CM), grazing land management (GM) and revegetation (RV)
under Art 3.4. Forest activities, in particular AR and FM, are key categories
for most MSs. Table A1.4
Activities accounted for by MSs (white cells) and key categories (K) All MSs except one reported on all mandatory
activities, and all but two reported on elected voluntary activities. In
general, biomass carbon stock changes are directly estimated whereas the three
other pools are frequently included elsewhere (IE) or not reported (NR). Under
the KP, a country must always provide transparent and verifiable information
that any non-reported pool, for mandatory and elected activities, is not a
source (in which case the notation key NR should be used). Although most MSs
reported most of the carbon pools, the completeness of reporting will need to
improve in subsequent inventories. The supplementary reporting required under
the KP as compared to the Convention reporting was submitted for the first time
in 2010 (for the first year of the commitment period 2008-12) and, inevitably,
certain areas were problematic the first time around.
The expert reviews show that the key difficulties appear to be to provide
information which demonstrates that: · units of lands are identifiable (i.e. that relevant units of land
or lands are precisely identified and can be traced in the future) · non-reported pools (SOM, DOM) are not sources · afforestation/reforestation activities are of a direct,
human-induced nature See Appendix 1 to this Annex and the Annual
European Union GHG inventory 1990 – 2008 and inventory report 2010 (Technical
report No 6/2010) for more information. Table A1.5. Completeness on reporting pools on KP
forest activities
8.1.2.3.
Concluding comments
The tables above suggest that: · for the coverage of land categories reported under the UNFCCC:
despite significant improvements in recent years, further efforts are required
from some MS (especially from the EU-12) to report GHG emissions from all the
“mandatory” categories under the Convention (i.e. forest land, cropland, grassland
and all land use changes; the other categories are considered “voluntary” under
the Convention). One particular aspect where further improvements are needed is
that areas of all land use categories should be reported, and the sum should be
constant over time and match the total area of the country. · for the coverage of activities under KP: all MSs except one
(Lithuania) reported the mandatory activities (AR, D); however, other two
countries (Portugal and Spain) did not report data for 1990 for elected activities.
Given the stringent mandatory nature of KP reporting, it is expected that these
gaps will be filled in the next 1 or 2 years. · for the coverage of C pools: under both the UNFCCC and the KP, the
assumption of no change in C stock can only be used in specific cases[47]. Furthermore, beyond these
specific cases, under the KP a country may omit the accounting of a pool (for a
mandatory or any elected activity) provided that transparent and verifiable
information is given showing that the pool is not a source (in this case, the
notation key “NR”, not reported, should be used). As a matter of fact, many MSs
apply the assumption of no change in C stock even when it should not be
applied, e.g. if forest land remaining forest land (or forest management under
the KP) is a “key category”, the tier 1 method (assuming no change in C stock)
cannot be used for DOM and mineral soil. Under the KP, this approach represents
a problem if it is not accompanied by transparent and verifiable information
which shows that the pool is not a source. In the first year of reporting under
the KP, the UNFCCC reviews raised this problem for several MS (see later
sections).
8.1.3.
Accuracy
Accuracy is defined by the IPCC GPG as “a
relative measure of the exactness of an emission or removal estimate. Estimates
should be accurate in the sense that they are systematically neither over nor
under true emissions or removals, so far as can be judged, and that
uncertainties are reduced so far as is practicable. Appropriate methodologies
conforming to guidance on good practices should be used to promote accuracy in
inventories.”[48]
In this sense estimates should be accurate so long as GPG is followed. However
the degree of accuracy may differ according to the approaches and methodologies
applied. GHG emissions/removals on land are
estimated as AD x EF, where AD is activity data (“area” in LULUCF), and EF is
emission factor (derived either as C stock change). IPCC (2003) recommends three approaches for
consistent land representation as basis for activity data estimation at
national level: APPROACH 1: BASIC LAND-USE DATA: area datasets likely to
have been prepared for other purposes such as forestry or agricultural
statistics. Frequently, several datasets will be combined to cover all land
classifications and regions of a country. There is no information on land use
changes. APPROACH 2: SURVEY OF LAND USE AND
LAND-USE CHANGE consists in a national or regional-scale assessment of not only the
losses or gains in the area of specific land categories but what these changes
represent (i.e., changes from and to a category). Thus, it includes more
information on changes between categories. Tracking land-use changes in this
explicit manner will normally require estimation of initial and final land-use
categories. APPROACH 3: GEOGRAPHICALLY EXPLICIT
LAND USE DATA requires
spatially explicit observations of land use and land-use change. The data may
be obtained either by sampling of geographically located points, a complete
tally (wall-to-wall mapping) or a combination of the two. The quality of GHG estimates is reflected
by the methodological tiers used. IPCC LULUCF GPG 2003
introduces three methodological tiers: The Tier 1 approach employs the basic
method provided in the IPCC Guidelines (Workbook) and the default emission
factors provided in the IPCC Guidelines (Workbook and Reference Manual)
with updates in this chapter of the report. For some land uses and pools that
were only mentioned in the IPCC Guidelines (i.e., the default was an
assumed zero emissions or removals), updates are included in this report if new
scientific information is available. Tier 1 methodologies usually use activity
data that are spatially coarse, such as nationally or globally available
estimates of deforestation rates, agricultural production statistics, and
global land cover maps. Tier 2 can use the same methodological
approach as Tier 1 but applies emission factors and activity data which are
defined by the country for the most important land uses/activities. Tier 2 can
also apply stock change methodologies based on country-specific data.
Country-defined emission factors/activity data are more appropriate for the
climatic regions and land use systems in that country. Higher resolution
activity data are typically used in Tier 2 to correspond with country-defined
coefficients for specific regions and specialised land-use categories. At Tier 3, higher order methods are
used including models and inventory measurement systems tailored to address
national circumstances, repeated over time, and driven by high-resolution
activity data and disaggregated at sub-national to fine grid scales. These
higher order methods provide estimates of greater certainty than lower tiers
and have a closer link between biomass and soil dynamics. Such systems may be GIS-based
combinations of age, class/production data systems with connections to soil
modules, integrating several types of monitoring. Pieces of land where a
land-use change occurs can be tracked over time. In most cases these systems
have a climate dependency, and thus provide source estimates with inter-annual
variability. Models should undergo quality checks, audits, and validations. Behind the approaches and tiers there are
various datasets and methods, used separately or in combination, to meet the
GPG requirements for estimation and reporting of GHG. Estimates from
country-specific data and methods (if calculated in accordance with GPG) are
likely to be more accurate than if they were prepared using default methods and
aggregated data sets.
8.1.3.1.
Underlying methods used to estimate activity
data
Given the heterogeneity of ecological and
socio-economic conditions in the EU-27, and for historical reasons, there is no
unique definition of different land uses across MSs. For the EU-15 the activity
data is estimated using approaches 2 or 3 (national forest inventories (NFI),
earth observation methods (EO) and land surveys (LS)) while some EU-12 MS still
use approach 1 (national statistics (NS)), see Table A1.6. Nevertheless, data
is provided through various official sources and may be underpinned by several
methods. Table A1.6. Data
sources for activity data in National Inventory Reports (NIR) 2010 MS || Reporting categories 5A Forest land || 5B Cropland || 5C Grassland || Other LU categories 5.A.1 || 5.A.2 || Distur-bances || 5.B.1 || 5.B.2 || 5.C.1 || 5.C.2 Austria || NFI || NFI || NS || NS || NS || NS || NS || NS Belgium || EO || EO || || EO, NS || || EO, NS || || NS Denmark || NS, NFI || NS,NFI || || NS, EO || || NS, EO || || NS, EO Finland || NFI || NFI || NS || NS || || NFI, NS || || NFI, NS France || NFI, LS || NFI || NS || NFI, LS || NFI, LS || NFI, LS || NFI, LS || NFI, LS Germany || NFI || NFI || NS || NS, NM, EO || NS, NM, EO || NS, NM, EO || NS, NM, EO || NS, NM, EO Greece || NS || NS || NS || NS || || NS || || NS Ireland || NFI, NS || NS, NM, EO || NS || NS || NM || NS || NM, EO || NS, EO Italy || NFI, NS || NS || NS || NS || NS || NS || NS || NS, EO Luxembourg || EO || EO || NS || EO || EO || EO || EO || EO The Netherlands || NFI, NM || NFI, NM || || NM || NM || NM || NM || NM Portugal || NFI, EO || EO, NS || NS || EO || EO || EO || EO || EO Spain || NFI, EO, NM || NS || NS || EO, NS || EO || EO || EO || EO Sweden || NFI || NFI || NFI || NFI || NFI || NFI || NFI || NFI United Kingdom || NS || NS || NS || NS || NS || NS || NS || NS Bulgaria || NS || NS || NS || NS || NS || NS || NS || NS Czech Republic || NS || NS || NS || NS || NS || NS || NS || NS Estonia || NS || NS || NS || NS || NS || NS || NS || NS Hungary || NS || NS || NS || NS || NS || NS || NS || NS Latvia || NFI, EO || NFI, EO || NS || NS || NS || NS || NS || NS Lithuania || NS, NFI || NS, NFI || NS || NS || NS || NS || NS || NS Poland || NS || NS || NS || NS || NS || NS || NS || NS Romania || NS || NS || NS || NS || NS || NS || NS || NS Slovakia || NS || NS || NS || NS || NS || NS || NS || NS Slovenia || EO, NS || EO, NS || || EO, NS || EO, NS || EO, NS || EO, NS || EO, NS Notation keys: NFI – National Forest Inventory; NS –
National Statistics (agricultural and forest statistics, management plans,
cadastral data); EO – Earth Observation methods (i.e. Corine Land Cover); LS
–Land Survey (i.e. permanent grid); NM – National Maps; and Empty cells –No
information reported/no reported pool. Land area Underlying methods used to determine the
“activity data” (i.e. land area) could be grouped according their
characteristics, as follows: · National statistics: for forest land data is provided by
ground measurement of area via stand-wise management plans of forests, updated
cyclically. Specifically data refers only to a certain type of land class (i.e.
usually forest and from/to conversions) and thus do not fully cover the country
territory. Data is then aggregated bottom up, but could be heterogeneous in
accuracy. National statistics provide data of areas at the end of year, thus
additional information on conversions must be derived from underlying sectoral
statistics with additional effort, e.g. the forest or land census. Data is
presented in tabular form, under various possible aggregations. For cropland
and grassland national statistics refers to datasets that are collected via
census or surveys on farms/land owners/administrators or public authorities,
based on questionnaires. In general, the method implies partial sampling every
year and a complete survey every 5-10 years. Data could be only presented as
registry or tabular, under various aggregations. Under cropland it covers the
information on land use and crops type, management approaches. · Systematic grids: data is provided by complex mathematic-statistical
procedure derived from geo-referenced systematic grids (i.e. NFI, Land surveys)
that cover entire/partial territory of the country. The method offers the
possibility to recalculate previous cycles with improved approaches or tools
for example geo-referenced virtual grids (without field sampling points). The
grid design differs from country to country, as well as the number of cycles
achieved. Data are presented in tabular form with the option of mapping by
additional effort. · Earth Observation: data are provided by mapping of land cover or
changes with successive images (satellite, aerial). The resolution is different
through time, with 1990 or earlier less accurate. Data is mapped, with the
option to develop tables and interoperate with other instruments. · Cadastral systems: “wall-to-wall” databases with ground measurement
of the land area. Additional methods are used for land classification (remote
sensing, field checks). Data covers all type of land categories within the
national territory. Measurements methods implement homogenous accuracy
standards. Data is both mapped and in tabular format, possible to enhance by
use of other instruments. Methods are combined in different ways from
one MS to another for each land sub-category, see Table A1.7, however, approach
2 and 3 are most prevalent. Table A1.7.
Methods used to determine the “activity data” for forestland (fl), cropland (cl) and grassland (gl) and category and their
characteristics (N= no. of MS reporting under respective method) Main datasets/source || Main characteristics of the method Land definition || Land identi-fication || Land classi-fication risk || Land conversion detection || Land traceability || Timing of data collection || Coverage of land category || Uncer-tainty National statistics (Nfl=5) (Ncl,gl=8) || LU || Non explicit || High || Low || Non traceable || Annual/ Periodic || Partial/ Total || Low Systematic grids (Nfl=11) (Ncl,gl=6) || LC/LU || Explicit || Low || High || Traceable || Periodic || Total || Low-Medium Earth Observation (Nfl=5) (Ncl,gl=6) || LC || Explicit || High || High || Traceable || Periodic /Annual || Total || Medium-High Cadastral systems (Nfl=4) (Ncl,gl=5) || LC/LU || Explicit || Low || High || Traceable || Annual || Total || Low Soil type distribution The activity data (area) of each
subcategory requires to be further disaggregated by main soil types (e.g.
mineral vs. organic soils). The available information on the methods used to
determine the area of mineral and organic soils suggest the following
distinction: ·
Associations of soil characteristics with land
cover or administrative units (regions, provinces, national) - expert judgement
association of land cover with certain soil type (especially used for organic
soils) or link to the soil databases with regional or national administrative
boundary (in order to derive unique values of reference C stocks at such level
for each land categories). Data are presented in a tabular format. ·
Soil maps - digital or digitized maps of soils
at various scales and mapping precisions. They may be very old (now digitized)
and time change of soil classification systems may affect their accuracy. Data
could be presented on various aggregation of soils (i.e. types, subtypes) and
mapping scales at certain moment in time (they could be very old). Data is
presented in map format and optionally in tabular format. ·
Systematic grids - data are collected via a
systematic grid or networks of permanent or temporary geo-referenced sampling
points, with repetitive sampling. The grid may cover total or partial territory
of the country (e.g. NFIs) and sampling points serve for other data collection
too. Data could be presented on type and subtype of soils in tabular formats,
with optional mapping (i.e. GIS). MSs more frequently apply approach 2 or 3,
but several MS do not determine activity data for different soil types at all. Table A1.8.
Synthesis of the datasets/methods used to determine the “activity data” for
SOILS and their characteristics (N= no. of MS reporting under respective
method). To note that not reporting MS, i.e. assuming no change in SOM pools
(N= 8) Main source/datasets || Main characteristics Soils definition || Soil type mapping || Traceability of sampling || Interopera-bility || Uncertainty Association with the land use/ cover/administrative data (Nfl= 6) || Low || Low || Low || Low || High Soil maps (Nfl= 8) || High || High || Low || High || Low/Medium Systematic grids (Nfl= 3) || High || High || High || High || Low/Medium Determination of "activity
data" for disturbance on forest land Major forest disturbances in the EU-27 are
forest fires and windstorms, with characteristic random occurrence in time and
space. Adequate information on the methods used to detect disturbances is not
always transparently and explicitly reported in National Inventory reports
(NIRs); often the CO2 loss from these events is included in the annual biomass
loss and therefore is not quantified separately. Nevertheless, accurate
reporting of the underlying data (i.e. area) is an essential requirement for KP
reporting. Table A1.9.
Synthesis of the datasets/methods used to determine the “activity data” for
disturbance and their characteristics (N= no. of MS reporting under respective
method) Datasets || Comments on methods Data not estimated (Nfl=3) || Data not available or reported as “NO” Sectoral statistics (Nfl=19) || Either affected area or volume is usually (rarely both) measured or estimated by ground measurement or visual assessment and made available in sectoral (not national) statistics National Forest Inventory (Nfl=3) || Area affected by disturbances is either directly derived by mathematic-statistical procedure in geo-referenced grids, often with the help of remote sensing datasets
8.1.4.
Underlying methods used to estimate emission
factors
Methods used by MSs to estimate emissions
and removals in the categories 5A, 5B and 5C in biomass, soil and dead organic
matter pools vary from default (Tier 1) to country specific (Tiers 2 and 3). Common Reporting Format (CRF) tables in GHG
inventories include a summary with methods and factors used. However, the
aggregation of this information (only by land use category and gas) is not
appropriate for a detailed analysis involving specific C pools. Therefore, the
detailed table A1.10 below has been elaborated by JRC taking into account more
specific information provided by each MS in its 2010 National Inventory Report
(NIR). “NE” is this tables indicates a reporting which is not complete
according to the IPCC methods and to the key category analysis done by the MS.
Note that it is often difficult to assign the method used for a pool to a
specific tier, and this table is subject of change upon improvements in time. || Forest land || Cropland || Grassland || FLrFL || LcFL || CLrCL || LcCL || GLrGL || LcGL || Biomass || DOM (1) || SOM Min || SOM Org (2) || Biomass || DOM || SOM Min || SOM Org (2) || Biomass || DOM || SOM Min (3) || SOM Org (2) || Biomass (4) || DOM || SOM Min || SOM Org (2) || Biomass || DOM || SOM Min (3) || SOM Org (2) || Biomass || DOM || SOM Min || SOM Org (2) AT || CS || CS,D || D || NO || CS || CS || CS || NO || D || D || CS,CS || NO || CS,CS || CS || CS || NO || D || D || CS,CS || CS || CS || CS || CS || NO BE || CS || CS,D || CS || NO || CS || D || CS || NO || NE || D || CS || NO || CS,NO || NE || CS || NO || D || D || CS || NO || CS || NE || CS || NO DK || CS || CS,D || D || CS || CS || CS || CS || CS || CS || D || CS || CS || CS,CS || CS || CS || CS || CS || D || CS || CS || CS || CS || CS || CS FI || CS || CS || CS || CS || CS || CS || CS || CS || CS || D || CS,D || CS || CS,NE || CS || CS || CS || D || D || CS,D || CS || CS || CS || CS || CS FR || CS || CS,D || D || NO || CS || CS || CS || NO || CS || D || NE || NO || CS,NE || CS || CS || NO || D || D || NE || NO || CS || CS || CS || NO DE || CS || CS,D || D || CS || CS || CS || CS || CS || D || D || CS || CS || CS,CS || CS || CS || CS || CS || CS || NE || CS || CS || CS || CS || CS GR || CS || D || D || NO || CS || D || D || NO || CS || D || D,D || CS || CS,NE || NE || NE || NO || D || D || NO || NO || NE || NE || NE || NO IE || CS || CS,D || D || CS || CS || CS || CS || CS || NO || D || CS,D || NO || NO,NE || NO || CS || NO || D || D || NE || CS,D || CS || NE || CS || CS IT || CS || D,CS || CS || NO || CS || CS || CS || NO || CS || CS || CS || CS || NO,NO || NO || NO || NO || CS || CS || CS,D || NO || CS || NE || CS || NO LU || CS || D || D || NO || CS || D || CS || NO || CS || D || NE || NO || CS || CS || CS || NO || D || D || NE || NO || CS || CS || CS || NO NL || CS || CS || D || NE || CS || D || NE || NE || NE || D || NE || CS || CS,NE || CS || NE || NE || D || D || NE || CS || CS || CS || NE || NE PT || CS || CS || CS || NO || CS || CS || CS || NO || CS || CS || CS || NO || CS,CS || CS || CS || NO || D || D || NE || NO || CS || CS || CS || NO ES || CS || D || D || NO || CS || D || CS || NO || CS || D || CS || NO || NO,NO || NO || NO || NO || D || D || NE || NO || NE || NE || CS || NO SE || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS,CS || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS || CS UK || CS || CS || CS || CS || CS || CS || CS || CS || CS || D || CS || CS || CS,CS || CS || CS || CS || D || D || NE || NO || CS || CS || CS || CS BG || CS || D || D || NO || CS || D || CS || NO || CS || CS || CS || NO || CS,D || NE || CS || NO || D || D || NE || NO || CS || NE || CS || NO CY || CS || D || D || NE || NE || D || NE || NO || NE || D || NE || NO || NE || NE || NE || NO || D || D || NE || NO || NE || NE || NE || NO CZ || CS || D || D || NO || CS || D || CS || NO || CS || D || CS,D || NO || CS, D || CS || CS || NO || D || D || CS,D || NO || CS || CS || CS || NO EE || CS || CS,D || D || CS,D || CS || CS || NE || CS,D || NE || D || NE || CS,D || NO,NO || NO || NO || CS,D || CS,D || CS || NE || CS,D || CS || NE || NE || CS,D HU || CS || D || D || NO || CS || D || D || NO || CS || D || D,D || NO || CS,D || CS || D || NO || D || D || D,D || NO || CS || CS || D || NO LV || CS || D || D || CS || CS || D || NE || CS || D || D || NE || CS || CS,NO || CS || NE || CS || D || D || NE || CS || NE || NE || NE || CS LT || CS || CS || CS || CS || CS || D || NE || NE || NE || D || NE || NE || NE,NE || NE || NE || NE || D || D || NE || NE || NE || NE || NE || NE MT || CS || D || D || NE || NO || NO || NO || NO || CS || D || NE || NO || NE || NE || NE || NO || D || D || NE || NO || NO || NE || NE || NO PL || CS || D || CS || NE || CS || CS || CS || NE || CS || CS || CS || CS || NE,NO || NE || NE || NE || D || CS || NE || CS || NE || CS || NE || NE RO || CS || D || D || NE || NE || D || NE || NE || NE || D || NE || NE || NE,NE || NE || NE || NE || D || D || NE || NE || NE || NE || NE || NE SK || CS || D || D || NO || CS || D || CS || NO || CS || D || NE || NO || CS,D || CS || CS || NO || D || D || NE || NO || CS || CS || CS || NO SI || CS || CS || D || NO || CS || CS || CS || NO || CS || D || CS || CS || CS,D || NE || CS || NO || D || D || NE || CS || CS || NE || CS || NO Table A1.10.
Analysis of methods and data used for reporting C pools (based on NIR 2010 and
CRF tables 2011 for the year 2009). Analysis carried out by JRC, March 2011. Notation key: DOM:
dead organic matter; SOM: soil; Min = mineral; Org: organic; "CS"
country specific data, associated either with IPCC method (tier 2) or
country-specific method (tier 3, if data are highly disaggregated). Note that
sometimes not all parameters involved in the estimation are truly
"CS" (e.g. root/shoot ratio and BEF are often taken by IPCC). However
it is expected that if "CS" is reported, the most important
parameters are truly "CS". "D" means that the default IPCC
emission factors are used in the estimation. D is tipically associated with
IPCC default method (tier 1). If the heading is in grey, D means that NO change
in C stock is assumed. "NE" means either country assumes the
emission/removal is negligible or not enough data is available for estimation.
"NO" means emissions or removals "not occuring" in a
country (it includes also "NA" - not applicable). Black heading means
that for these pools the IPCC allows the assumption of no change in C stock
under tier 1 (note that if the category is a key categroy, in theory higher
tiers should be used). For DOM in CL and GL, the assumption of no C stock
change is also valid under tier 2. Grey cells denote
key categories according to either level or trend analysis done by the country
(FR, DK, LT did not yet perform a key category analysis, as of March 2011). In
general, key categories should be reported using higher tiers. However note
that, when reviewers assess the adequacy of the methodological choice (tiers)
in relation to key categories, should also consider "national
circumstances" (i.e. the availability of data/resources/capacity in the
country). This means that in some cases tier 1 may be also accepted for key
categories. Notes: (1) for DOM
under "FLrFL" the 2 notations separated by a comma mean: first one
refers to DW (dead wood), second to LT (litter); (2) for organic soil
"NE" is used if the country reports some area of organic soil but no
emissions are associated to it; (3) for mineral soil on CL and GL the 2
notation keys separated by comma mean that the country uses IPCC default method
(i.e. tier 1 if associated with D data, or tier 2 if associated with CS data);
in this case, the first notation key refers to "reference C stock",
and second to "C stock change factor" (see IPCC-GPG for details). A
cell with a single "CS" indicate a country-specific method and data
(i.e. tier 3 if data are highly disagreagated); and (4) for biomass under L c
CL, "conversion to cropland", the 2 notation keys used mean: first
one refers to FLcCL and second to GLcCL. When comparing the absolute levels or
trends of Implied Emission Factors (IEFs) across MS, much caution should be
used. Indeed, in some cases, large differences may be attributable to the
different estimating or reporting methodology and they do not truly reflect the
different intensity of emissions and removals. For example, some IEFs may be
significantly affected by new areas entering a given category. Furthermore, the
fact that not all countries use the 20-year IPCC default transition period for
land use change categories means that the corresponding emission factors are
not fully comparable across all MSs. Determination of “C stock change factors”
for Forest Land Based on MS’ NIRs, information on
underlying methods used to determine the C stock change factors in forest land
is generally available for biomass, but less for SOM and DOM. · Biomass: Information on C stock change in
the biomass pool is the most complete in NIRs, with rich information on the
underlying method used for collection (Table A1.11). In fact, all MS apply Tier
2 or 3 methods. Table A1.11.
Synthesis of the datasets/methods used to determine the “C stock change
factors” for BIOMASS and their characteristics (N= no. of MS reporting under
respective method) Data source || Method || Comments on method National Forest Inventory (Nfl=17) || Statistical procedures in geo-referenced grid || Data obtained by complete and repetitive field measurements in permanent and/or temporary plots. Proxy parameters are the dimensions of individual trees in the sampling plots. Stand wise forest inventory (Nfl=6) || Partial sampling of stands || Data is obtained by both field measurements (i.e. old stands) and derived from yield tables (i.e. for young stands). Proxy parameters are the stand descriptive characteristics. Various statistics (Nfl=2) || Modeling || Source data could be NFI or combination of other data (i.e. afforestation data, yield tables, wood harvest) Despite the few basic methods, there is a
lot of heterogeneity on implementation characteristics of each method across MS
(i.e. grid density, plot characteristics, sampling procedures). · Dead organic matter: Information on
underlying methods C stock change in this pool is rather poor in the NIRs,
still with many MS not reporting. The majority of MSs does not report a change
(Tier 1), see Table A1.12. Table A1.12.
Synthesis of the datasets/methods used to determine the “C stock change
factors” for DEAD ORGANIC MATTER and their characteristics (N= no. of MS
reporting under respective method) Data source || Method || Comments on method Not reported (Nfl=16) || NA || Reporting according Tier 1 IPCC (no change) National Forest Inventory (Nfl=7) || Statistical procedure in geo-referenced grids || Usually only dead wood data is collected (not the litter). Annual estimates are derived by mass or C stock difference over successive cycles and interpolation Various data (Nfl=4) || Modeling (process based or bookkeeping models) || Source data could be NFI, other statistics (i.e. afforestation, harvest, yield tables) or simple regressions based on other data (i.e. stand aboveground biomass) · Mineral soils: It is rarely clear from NIRs
what soil classification systems are used and how data is processed, while the
method to collect data is provided (Table A1.13). For land conversion to/from
forest the most used method is that of “reference C stocks” which allows an
annual constant linear change between “initial” and “final” C stocks. More than
half of MS apply Tier 1 (no change in C stock) in forest remaining forest. Table A1.13.
Synthesis of the datasets/methods used to determine the “C stock change
factors” for MINERAL SOIL and their characteristics (N= no. of MS reporting
under respective method) Data source || Method || Comments on method Not estimated (Nfl=13) || NA || Reporting according Tier 1 IPCC (no change) IPCC default values (Nfl=1) || IPCC default values || Unique national reference C stocks assumed equal to IPCC default value National Forest Inventory and soils databases (Nfl=11) || Statistical procedure in geo-referenced grids || Time difference and annual interpolation based on repetitive sampling (i.e. national soil database, NFI, ICP Forests, research projects) National Forest Inventory and other various data (Nfl=5) || Process based or bookkeeping modeling || Source data could be NFI, or other statistics (i.e. afforestation, harvest, yield tables) or simple regressions · Organic soils: The definition of soil C pool
differs across MS in terms of the threshold between organic and mineral soils
and the treatment of peatland and drained areas with organic soils. There are
only a few methods used for estimation of organic soils (Table A1.14). Tier 1
is the most used method for organic soils. Table A1.14. Synthesis
of the datasets/methods used to determine the “C stock change factors” for
ORGANIC SOIL and their characteristics (N= no. of MS reporting under respective
method) Data source || Method || Comments on method Not estimated (Nfl=18) || NA || Not reporting or not occurring or (in disagreement with IPCC GPG LULUCF) assumed neutral IPCC default emission factor (Nfl=4) || IPCC default values || Reporting under Tier 1 National Forest Inventory or other data (Nfl=3) || Modeling || Process based modeling with inputs from NFI or other data (i.e. afforestation, harvest, yield tables) · Emission factors for disturbances on forest land: The main source of data are the NFIs or stand wise forest inventories,
combined with specific statistics managed by forest authorities. The wood volume
lost in disturbance events is usually measured or estimated by ground
measurement or visual assessment and provided in statistics. Another method
considers the average estimate of standing stock (derived from NFIs database). Determination of “C stock change
factors” for cropland and grassland Information on C stock change factors in
cropland is rather poor in NIRs. The basic underlying methods do not differ
significantly between cropland and grassland on C stock change in soils. · Biomass: On cropland, only C stock change in
woody permanent crops is estimated (i.e. orchards, vineyards, olive groves).
Annual biomass is only computed by few MS, and sometimes it is done with the
purpose to assess the organic matter input into soils (which drives a model for
soil emissions). Changes in biomass are mainly reported as not occurring on
grassland (following IPCC tier 1), and default values are also commonly applied
to cropland (Tier 1). Table A1.15. Datasets/methods used to determine the “C
stock change factors” for biomass (N=
no. of MS reporting under respective method on CL or GL) Datasets || Comments on method Not reported (NCL= 8) (NGL= 22) || Not yet estimated or assumed “no change” IPCC default values (NCL=10) (NGL=0) || IPCC default factors. 2 MS included here report CS data for bioenergy/biomass plantations Basic country specific data (NCL= 5) (NGL=0) || National data available (i.e. from research). 2 MS included here report some type of permanent crops by modeling Various inputs to models (NCL=2) (NGL=3) || Process based modeling with various inputs (i.e. non-forest NFI sampling, research data) · Mineral soils: Reporting of this pool is
still rather poor because of lack of consistent and adequate data. Also, the
methodological information in the NIRs is not always adequate. Where available,
the data is often provided by heterogeneous databases (i.e. various
methodologies, sampling year). Tier 1 is applied by four MS; Tier 2 or 3
methods are applied by about half MS. Table A1.16.
Datasets/methods used to determine the “C stock change” for mineral soils (N= no. of MS reporting
under respective method on CL or GL) Datasets for C stock in mineral soils || Comments on method Not reporting (NCL= 8) (NGL= 11) || Not yet estimated or assumed (in discordance with IPCC LULUCF GPG) “no change” for CL IPCC C stock default values (NCL= 4) (NGL= 4) || IPCC default values Country specific reference C stocks (NCL= 9) (NGL= 6) || C stock reference is developed on each land use either on province or national scale from soil database Various inputs to models (NCL= 4) (NGL= 4) || Process based modeling with various inputs (i.e. NFI, agricultural data; research data for GL) Adjustment factors data (Flu, Fmngm, Fi) || Expert based adjustment of IPCC default values || Only 1 MS developed own factors based on time series of field measurements · Organic soils: while
few MS report emission from organic soils under cropland or grassland, the
emissions are usually relevant. On grassland, reporting of organic soils is
particularly poor because of lack of consistent and adequate data. Lack of
transparency and explicit information characterizes this issue in NIRs. Table A1.17.
Datasets/methods used to determine the “emission factors” for organic soils (N=no. of MS reporting
under respective method on CL or GL) Datasets/source || Comments on method Not reporting (NCL= 12) (NGL= 12) || Not yet estimated or assumed “no occurring” IPCC default values (NCL= 7) (NGL= 8) || Emission factor provided by the IPCC GPG Country specific data (NCL= 6) (NGL= 5) || Data from research used for reporting either based on CS emission factors (i.e. heterotrophic respiration) or by modeling (i.e. input from NFI)
8.1.5.
Uncertainties
This section presents the estimated
uncertainties at EU-15 level, based on the (often incomplete) information
available in MS’ NIRs. Uncertainties for AD and EF are reported as simple
averages of MS’ reported data, while uncertainties for GHG emission/removals
are estimated based on the aggregation (by error propagation) of MS estimates.
In general, these estimates at EU-15 level can be considered broadly valid also
for the whole EU. More infomation can be found in the EU NIR 2010. The uncertainty of the activity data (AD)
varies across MS and land (sub)categories within the MSs. The uncertainty level
depends on the original purpose of datasets, land use definitions and their
consistent use in time, spatial resolution, reference years as well as land
data processing techniques which usually introduce additional uncertainty in
GHG estimations. For forest land remaining forest land the uncertainty at EU-15
level is estimated to be about 12%, as a simple average, with the lowest value
reported by Germany and the UK (~1%). For lands under conversion to forest
land, MS reported very different values, with an overall simple average of 15%
and the highest value reported by Italy (75%). For all other land categories,
the average uncertainty is around 20% for lands remaining the same land and
apparently less than 20% for lands under conversion. The uncertainty associated with the emission
factors/C stock change factors(EF) is in the order of 60–85%, as a simple
average, for all GHG and land uses in the EU-15, and a little higher on lands
under conversion. It also shows a rather high fluctuation among MSs, because of
the generally high number of parameters involved in its calculation and the
variety of the methodological approaches. Again, the direct comparability among
MS is low as often it reflects combined uncertainty with the activity data. The
uncertainty of the EF is higher for the land subcategories in the territories
of MS with high disturbance levels like forest fires (i.e. Portugal and Greece)
or organic soils (i.e. Finland). The aggregated estimated uncertainty of the
annual EU-15 emissions and removals is 32% for forest land, grassland and
cropland combined; however, it ranges from 25% in land converted to forest to
105% in cropland remaining cropland (see Table A1.18). For the new 10 MS (i.e.
excluding Malta and Cyprus) similar ranges of uncertainty of activity data can
be expected, but higher levels for C stock change factors (JRC 2010a). Table A1.18.
Uncertainty across the EU-15 by subcategories and GHG in 2008 (half of 95 %
confidence interval of averages, by error propagation with individual MS E/R) Inventory category || Category uncertainty for EU (%) || Total estimated emission (+)/ removal (-) in 2008 (Gg CO2) || Uncertain amount (GgCO2) FL r.FL (5A1) || 30% || -276,794 || 81675 LcFL (5A2) || 25% || -49,779 || 12446 CLrCL (5B1) || 105% || 19,184 || 20216 LcCL (5B2) || 30% || 45,341 || 13484 GLrGL (5C1) || 87% || 11,923 || 13756 LcGL (5C2) || 49% || -22,077 || 10796 Total** || 32% || -280,018 || 88590 || || || Forest land (5A) || 26% || -326,573 || 84761 Cropland and grassland (5B+5C) || 63% || 46,555 || 31784 *It should be noted
that estimates of the aggregated EU-15 level uncertainties may slightly vary
also depending on the method used to aggregate the available information at MS
level. Subcategory uncertain amounts do not sum up to the overall EU 15's
unceratin amount due to compensation over the error propagation procedure.
Uncertainty is computed as two standard deviations (2σ for 95 % confidence
interval). ** This estimate
excludes Wetland, Settlements and Other land. Taking these land categories into
account, the total uncerainty estimate amounts to 35%. Notes: Much care
should be used when comparing uncertainties of specific LULUCF categories with
others sectors. While all the other sectors are sources of GHG, LULUCF may
either be a source or a sink. This may profoundly affect the estimate of %
uncertainty, depending at which level the analysis is done. E.g. If the
subcategory “cropland remaining cropland” is a sink of -50 MtCO2 +/-10%, and
“land converted to cropland” is a source of +55 MtCO2 +/-10%, the whole
cropland category will be a source of +5 MtCO2 with a level of uncertainty
close to 100%. By way of comparison, Table A1.19 shows the
uncertainty related to estimates in the agricultural sector (non-CO2
GHG). Uncertainty in this sector is 68% on average but varies from 12% (enteric
fermentation) and 156% (agricultural soils). Table A1.20 shows the overall
uncertainty estimates for other sectors, based on the magnitude of emissions.
It is clear that the level of uncertainty in LULUCF is relatively large
compared to e.g. fuel combustion (1.5%), transport (6%), industrial processes
(5%) and waste (21%), but is similar to that of fugitive emissions (32%) and
smaller than the uncertainty in the agriculture sector. All sectors other than
LULUCF are already part of the EU's GHG reduction commitment. Table A1.19.
Uncertainty across the EU-15 by subcategories and GHG in 2008 (half of 95 %
confidenceinterval of averages, by error propagation with individual MS E/R) Inventory category || GHG || Category uncertainty for EU (%) Enteric fermentation (4A) || CH4 || 12% Manure management (4B) || CH4 || 26% Manure management (4B) || N2O || 61% Rice cultivation (4C) || CH4 || 20% Agricultural soils (4D) || CH4 || 50% Agricultural soils (4D) || N2O || 156% || || Total || CH4+N2O || 68% Source: Leip (2010) Table A1.20.
Uncertainty estimates of EU-15 GHG emissions and removals Source: EEA (2010)
8.1.6.
Recalculations
A large number of countries carried out
important recalculations in the last GHG inventory. Recalculation are usually
due to change in methods (e.g. from stock change to gain-loss), new C pools
reported for the first time, or new data (e.g. new NFI). Recalculations are an
inherent characteristic of the LULUCF sector and are generally more significant
than in other sectors. This is illustrated by Figure A1.1 which shows
differences between 2009 and 2010 amounting to five, ten and 25% of MSs' 1990
GHG emissions. This has implications for the possibility of including LULUCF in
existing policy frameworks to the extent that they require annual compliance
with emission limits as well as corrective action when a MS's (under the ESD)
or installation's (under the EU ETS) emissions exceed its limit. Figure A1.1.
Extent of recalculations for the year 2007 in “forest remaining forest”
(difference 2010-2009 submissions), expressed as % of total 1990 GHG emission
of each MS.
8.1.7.
Time-series consistency and comparability
between MSs
Comparability can be looked in at least two
ways; within a MS over time ("time-series consistency") and between
different MSs. The former can be generally considered scarcely a problem and
the following sections therefore focus on some of the factors that affect
comparability between MS. It follows from earlier sections that there is
diversity in estimation methods amongst MS. This is highlighted by e.g.
Lawrence et al. (2010) who also note that variations in definitions make direct
comparability difficult. Some of the differences in definitions are discussed
below.
8.1.7.1.
Definitions
Forest definition The parameters used to define forest under
the KP are presented in Table A1.21. Definitions vary across MSs. While consistency within the country in terms of time and space is achieved
by most MS, direct comparability accross the MSs is currently not possible as
the parameters vary across minimum crown cover, minimum hieght, minimum area
and minimum width. Although diversity in methods inevitably contributes to
differences in estimates, if forest area sampling and estimators are unbiased,
then detrimental effects on harmonised reporting as a result of their diversity
are minimal (Lawrence et al., 2010). Table A1.21.
Selection of parameters for defining “forest” under the Kyoto Protocol Definition of land use changes Definitions of land use changes may differ
among MS, and this introduces some difficulty in comparing estimates. For
example, some MS define deforestation based on specific documentation (e.g.
permissions), while other MSs consider that if after a given lapse of time from
harvest (e.g. 5 or 10 yrs) regeneration did not occur, than it is
deforestation. Data on AR and D areas suggest there may be a problem of comparability
among MS (e.g. France has D area hundreds times that of Italy, Portugal
hundreds times that of Spain). It is clear that for some MS data for AR and D
are still preliminary. Many MS noted the difficulty of reporting on land use
changes, because they are usually small and scattered events. Figure A1.2.
Comparison of ARD area in the EU-27 Definitions of carbon pools The wide variation of definition of the
pools amongst MSs (aboveground and belowground biomass, soil organic matter,
dead organic matter) is notable. The effect on overall accuracy of the estimation of carbon stock changes and other GHG emissions
related to various pools definition is likely low, but also difficult to assess
in quantitative terms (i.e. for non-considered part of the pools, like trees
under the threshold diameter). The variation in definitions affects the comparability amongst MS but likely not the
completeness, to the extent that the share of pools which is reported by one MS
under some category is reported by others under a different category.
Nevertheless, a pool reported under other pools may introduce higher uncertainty in the estimation (caused mainly by the different turnover),
but it can be duly justified for practical reasons (i.e historical data
availability, measurement errors). For example, the minimum DBH (breast height
diameter) for trees measurement in NFI varies from 0 cm for 5 MS, < 5 cm for
3 MS and < 10 cm for 5 MS. Also, soil organic matter (SOM) is estimated for
different depth (30 cm to 100 cm) across MS. Dead organic matter pool (DOM, litter
and dead wood) also differs in terms of type (standing,
laying), threshold diameter and height, and duration since laying down (which
defines the decomposition period).
8.1.8.
Verification activities
Currently, there is poor implementation of,
or at least little information on, verification activities by EU MS. Despite
IPCC GPG LULUCF provides a number of possible specific approaches for
verification, several MS included verification activities in the Quality
Assurance (QA)/Quality Control (QC) process and only focus on double checks of
input data used for the estimation of GHG. There are few specific cases of
verification of the outputs (e.g. C stocks changes): Germany reports on the
calculation of the C stocks and C-stock changes in biomass for forestland by
two different forestry institutes and estimates are in “good agreement”. Italy
reports on current implementation of an interregional project to carry out
atmospheric emission inventories at local scale, with a module on the
estimation of forest land related emission/removals (in 7 out of the 20 Italian
regions), the results of which will allow the validation of both methodology
and estimates at country level. Finland reports the establishing of a network
for GHG monitoring of drained organic soils. The UK implements a number of
research projects on the effect of afforestation, effect of cultivation and
re-sampling in the framework of National Soil Inventory as part of GHG
inventory programme. Sweden confirms the trend of estimated removals by
implementing the default method with own data. Also, it reports that process
based models and field measurements agreed on organic matter and soil organic
carbon in forests. Denmark performs field collecting samples in order to check
the outputs of the model which is used for reporting. In terms of disturbances, Forest fire -
EFFIS (European Forest Fire Information System, http://effis.jrc.ec.europa.eu/)
is a potential candidate for verification at the continental scale. It is a
tool for rapid damage assessment and active fire detection which may be
assessed in the way of verification of emissions from fires. Practically all
the data are delivered in a harmonized scheme and in the standard European
spatial reference system ETRS-LAEA which ensures the most recent MODIS coverage
of Europe. In conclusion, although verification
activities are clearly a key element for ensuring inventory quality and
increasing confidence on results, currently there is still poor implementation
of, or at least little information on, verification activities at EU level. It
is expected that more attention will be paid on verification in coming years,
and that a larger role in this context will be played by models.
8.1.9.
Costs of improving monitoring
Reporting under the
UNFCCC is mandatory for land categories for which IPCC GPG exists, i.e. forest
land, grassland and cropland, and all land use changes (for all land
categories). In addition, the KP requires reporting for a number of mandatory
and elected activities and adds obligations to the UNFCCC reporting. This
requires action by MSs. Tier 1 should be applied as a minimum in reporting but
it is good practice to use tier levels 2 or 3 for key categories.[49] The objective for monitoring and reporting set out in this impact assessment
is that MSs comply with the IPCC GPG. More efforts are required to meet this
objective. A two-step approach was used in order to
estimate the associated costs (see discussion in Section 4.2 of the main text): · Step 1 – Achieving completeness in the reporting of all mandatory
categories and pools at a minimum level of tier 1, · Step 2 – Increasing the accuracy of the reported key categories and
pools to a minimum of tier 2. It is, however, a challenge to estimate the
related costs and the following limitations should be borne in mind when
interpreting the results: · There is no unique way of reaching the objective because the IPCC
GPG allows for different methodologies and approaches to be applied according
to national circumstances. A number of tools exist for representing land areas
(e.g. use of existing data, remote sensing and ground-based surveys) and for
deriving emission factors (e.g. default values, literature, measurements and
models), and these can be combined in a number of ways. For the purpose of this
assessment, repeated soil sampling campaigns were simulated to derive country
specific emission factors for soils (required for tier 2) and that can be
combined with largely existing activity data. · It is a complex task to establish the current monitoring capacity,
or the baseline, in MSs. Here the information on methodologies and approaches
reported by MSs to the UNFCCC in 2010, and analysed by JRC (2011a), was used,
see Table A1.3 (completeness of reporting) and Tables A1.6 and A1.10 (type of approaches
and methodologies). · The availability of literature on monitoring costs is limited. A
mixture of sources, based on actual experience and studies, was used to obtain
the necessary assumptions to derive an interval indicating the potential costs.
8.1.9.1.
Step 1 – Achieving completeness in the reporting
of categories and pools at a minimum level of tier 1
Reporting on biomass in forest land is
generally complete. Only a few MSs did not report (in 2010) on this pool in one
of the sub-categories under forest land. On the contrary, most MSs did not
report on soil in one or both of the sub-categories under forest land, cropland
and grassland. The cost of
improving the completeness to a minimum of tier 1 level of accuracy is,
however, assumed to be reasonable, both for biomass and for soils; whilst it
requires compiling activity data from different sources, the data is generally
available in MSs and/or from European initiatives (e.g. LUCAS[50] and CORINE) and can be combined with IPCC default values (tier 1).
8.1.9.2.
Step 2 – Increasing the accuracy of the reported
key categories and pools to a minimum of tier 2
This step focuses on improving the level of
accuracy of monitoring and reporting of stock changes in soils in forest land,
cropland and grassland as this is where the most significant gaps in reporting
exist, see Table A1.10. SE, DK, FI, DE and the UK apply high level tiers to the
soil pool in all land categories but most MSs use tier 1 or do not report at
all on soil in at least one of the land categories. On the basis of what is required by the
IPCC GPG (2003), it is assumed that activity data from
different sources available in MSs and/or from European initiatives (e.g.
updates of LUCAS and CORINE) would provide sufficient information and spatial
resolution to be used for tier 2 and that the cost for this component therefore
is low. For the purpose of simulating the cost of increasing the level of
accuracy and to derive country-specific emission factors it is assumed that the
required data would be collected through repeated soil sampling campaigns in
order to capture the annual rates of emissions and removals, calculated as the
difference in stocks over time divided by the inventory time period. A
comprehensive review of the soil inventory and monitoring activities in the EU
was carried out under ENVASSO[51] and evaluated the extent to which existing soil monitoring networks
adequately represent European soil typological units, land use/cover, specific
soil criteria such as soil organic carbon and bulk density (which are needed to
measure changes in carbon stocks). It also detailed the existing national
networks, their purpose, sampling strategy, analytical methods used and number
of monitoring sites (around 35 000 for soil organic carbon). Procedures
and protocols appropriate for inclusion in a European soil monitoring system
were identified and documented and, finally, a soil monitoring system was
defined that comprises a network of geo-referenced sites at which qualified
sampling process is being or could be conducted. In addition, the BioSoil
demonstration project (Hiederer et al., 2011) is one of several studies under
Forest Focus[52] and was initiated to develop the monitoring scheme by means of
studies, experiments, demonstration projects and testing on a pilot basis and establishment
of new monitoring activities. The following steps were taken: 1.
The sampling density was determined based on a
review of plot sizes ranging between 6 400 ha, 18 000 ha (LUCAS), 19 600 ha
(Forest Focus and Biosoil) and 30 000 ha (which is the EU median density,
Arrouays et al., 2008) per plot. Although it should be noted that the number of
plots also depends on the desired level of certainty and the sampling interval
(Mäkipää et al., 2008). A consideration for monitoring and estimating soil organic
carbon is that it may be desirable to stratify mineral and organic soils and
sample within those strata to obtain sufficient data for the organic soils. 2.
The number of samples per plot was set at 1
composite soil carbon sample (consisting of 2-4 soil samples) and 1 composite
sample for bulk density (IPCC, 2003). 3.
Case studies show that the cost per sample
ranges between €6-16 as regards the laboratory cost (Stolbovoy et al., 2007)
which constitutes about 25% of the total cost (Mäkipää et al., 2008), i.e.
total cost per sample amounts to €24-64. Mäkipää et al. (2008) found that the
cost per sample was €47 but estimated a fixed cost per plot at €230. 4.
The number of sites needed per MS was determined
by dividing the total area of forest land, cropland and grassland by the
sampling plot density where the land area is a key category. 5.
The cost per MS was calculated by multiplying
the number of sites with the total cost per site, divided by the time interval
between two different sampling campaigns, ranging between 20 years for tiers 1
and 2 (IPCC, 2003), 10 years (Arrouays et al., 2008) and 3-5 years for tier 3 (IPCC,
2003). However, the cost obtained from 1-5 above
assumes that all MSs would require investments in soil monitoring. As shown in
A1.10 this is not the case, neither for all MSs nor for all land categories.
Therefore an additional step was taken: 6.
The cost related to those land categories for
which MSs already apply tier 2 or 3 methodologies was deducted from the cost
obtained in 1-5. As noted above, MSs are required to report
to report under the UNFCCC and the KP, using tier 1 as a default and tiers 2 or
3 for all key categories. The cost of meeting these criteria should therefore
be attributed to the ratification of the Convention and the KP and the adoption
of the IPCC GPG, and not the potential inclusion of LULUCF in the EU's GHG
reduction commitment. The fixed costs of monitoring a network of
plots were not assessed, but it is assumed that use could be made of existing
networks, as shown by Kibblewhite et al. (2008), and so limiting or cancelling
the cost of additional plots. Additional reporting costs (e.g. to adapt UNFCCC
data to KP format) are also judged to be negligible given the current reporting
requirements under the UNFCCC and the KP.
8.1.9.3.
3 – Harmonisation
In addition to the IPCC GPG, additional
efforts to harmonise monitoring and reporting in the EU should be promoted and
this would require resources. The associated costs have not been estimated. Great effort has already gone into harmonizing forest and soil
surveys. The findings from BioSoil (EC, 2003) clearly ask for better
harmonization of methods to allow arriving at comparable results between
national surveys, but also to provide temporal consistency. Otherwise
differences in methods overshadow changes in the field and cannot be separated
to estimate C stocks and GHG emissions. Table A1.22 summarises the assumptions made
for two cost scenarios, one low and one high. Table A1.22
Summary of assumptions about sampling parameters Parameter || Value || Low cost || High cost Sampling density || 19 600 ha || 6 400 ha Number of samples per plot || 2 composite samples (1 soil carbon, 1 bulk density) || 2 composite samples (1 soil carbon, 1 bulk density) Sample cost || €24 || €64 Fixed cost per plot || €238 || €238 Sampling interval || 20 years || 8 years || 4 year
8.1.9.4. Results
Table A1.23 shows the results of Step 2,
using two different assumptions about the baseline. If all MSs require
investments in soil monitoring to move from tier 1 to tier 2 or 3, the annual
cost for the EU-27 would be between 0.5 and 2.1 mln €/year, assuming a sampling
interval of 8 years. However, and as shown in Table A1.10, a number of MSs
already have the required capacity which means that the additional cost is likely
to be lower, between 0.4 and 0.5 mln €/year. As noted above, MSs are already
obliged to use tier 2 or 3 for key categories in UNFCCC and KP reporting.
Hence, whilst the costs to MSs of improving monitoring are positive, the cost
of the policy objective is zero. Arrouays et al. (2008) estimated the number
of additional sites needed to detect a relative decrease of 5% of the national
mean of topsoil organic carbon contents according to national statistics on
variances. If the same cost assumptions are applied to their estimate, the
resulting costs would be €0.9 to 1.2 mln per year with an 8 year sampling
interval. On the other hand, soil sampling was carried out under the auspices
of LUCAS[53] in 2009 at an additional cost of only €1.2 mln (€0.2 mln per year).
This shows that significant cost savings to be had through coordinated and
systematic schemes. Table A1.23.
Indicative costs of improving the level of accuracy of MRV Frequency of sampling* || Annual cost for EU-27 (mln €/yr) || Low || High || || || Baseline assumption: all MSs require investments in soil monitoring every 4 yrs || 1,1 || 4,2 every 8 yrs || 0,5 || 2,1 every 20 yrs || 0,2 || 0,8 || || || Baseline assumption: some MSs (based on Table A1.9) require investments in soil monitoring every 4 yrs || 0,7 || 0,9 every 8 yrs || 0,4 || 0,5 every 20 yrs || 0,1 || 0,2 || || Cost of policy requiring improved MRV according to IPCC GPG standard under both baseline assumptions** || 0 || 0 || || Cost of harmonisation || Not estimated || Not estimated Notes: *According
to Arrouays et al. (2008) a time interval of about 10 years would enable the
detection of some simulated large changes in soil organic carbon in most
European countries. IPCC uses 20 years as a default (tiers 1 and 2) and 3-5
years for tier 3. An 8 year interval is used here as the central scenario to
match the compliance period of 2013 to 2020. ** The cost of the policy is what
would be required in addition to the meeting the obligations under the UNFCCC
and KP; MSs are already required to apply tier 2 or 3 for key land categories,
but only some MS comply.
8.1.10.
Appendix 1 – Overview of problems emerged during
2010 review and answers by MS
The EU QA/QC and UNFCCC review 2010
revealed several issues related to achievement of the reporting requirements
under the KP: · To demonstrate that a non-reported C pool is not a source. This is a
problem for soil C, especially for land remaining in the same category (e.g.
forest remaining forest). · To identify and track LUC over time ("land
identification") · To estimate appropriate EF for organic soils A summary is provided in Table A1.24. MSs
have since provided more detailed explanations for the issues raised. It is
likely that at least in part the problems are related to transparency in
reporting (i.e. not enough background explanations provided). Table A1.24.
LULUCF issues raised in the review of the 2010 submission for the Kyoto
Protocol MS || Main issues || Specific issues || Likely impact on reporting and accounting Notation key: Small < 1 MtCO2eq. Medium 1-5 MtCO2eq. Large > 5 MtCO2eq. Austria || AR || Land identification || Medium Denmark || ARD, FM, CM, GM || “C pool not reported” w/o demonstrating that it is not a source (ABG, BGB, LT, DW, SOM) || Small AR || “C pool not reported” w/o demonstrating that it is not a source (SOM –organic soils) || Small AR || “C pool not reported” w/o demonstrating that it is not a source (LT) || Small CM, 1990, 2008 || SOM || Probably medium GM, 1990, 2008 || SOM || Probably medium Finland || No issues || No issues || France || AR || Land identification || Large FM || Emissions from fires || Small-Medium D || “C pool not reported” w/o demonstrating that it is not a source (DW) || Small D || Lime application || Small Germany || ARD, FM || National system - land identification || Difficult to assess Greece || D || Incomplete land coverage || Probably small ARD, FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Ireland || D || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Italy || No issues || No issues || Netherlands || AR,D || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Portugal || CRF table 1990 || Only notation keys || Difficult to assess ARD, FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Spain || AR || “C pool not reported” w/o demonstrating that it is not a source (SOM) || Medium FM || “C pool not reported” w/o demonstrating that it is not a source (SOM) || Medium CL, 1990 || SOM, ABG, BGB || Probably medium Sweden || No issues || No issues || UK || D || Incomplete land coverage || Probably small D || Information that demonstrates that deforestation activities began on or after 1 January 1990 || Probably small D || Emissions from wildfires on deforestation lands || Small Czech Republic || AR, FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Estonia || ARD || Land identification || Probably small ARD || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small D || Distinguishing of harvesting/forest disturbance from deforestation || Potentially large Hungary || KP tables || National system || Difficult to assess Latvia || AR || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small D || “C pool not reported” w/o demonstrating that it is not a source (ABG, BGB, LT, DW, SOM) || Probably small FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Small Poland || ARD, FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Medium Romania || KP tables || National system || Difficult to assess AR || Land identification || Probably small AR || “C pool not reported” w/o demonstrating that it is not a source (LT, DW,SOM) || Medium FM || “C pool not reported” w/o demonstrating that it is not a source (LT, DW, SOM) || Medium FM || Land areas || Unknown Slovakia || ARD, FM || National System || Difficult to assess ARD || “C pool not reported” w/o demonstrating that it is not a source (LT, DW) || Small Slovenia || D || “C pool not reported” w/o demonstrating that it is not a source (LT) || Small Luxemburg || ARD || KP tables not submitted in time || Likely small Lithuania || ARD, FM || National system || Difficult to assess Bulgaria || ARD || Land identification and “direct human induce” || Medium ARD || “C pool not reported” w/o demonstrating that it is not a source (DW) || Small Notation keys: ABG
– Above ground biomass; BGB – Below ground biomass; LT – Litter; DW – Deadwood;
and SOM – Soil organic matter.
8.2.
Annex II – Impacts on accounting of threshold
values (natural disturbances) and caps (forest management)
8.2.1.
Implications of different triggers for the
application of provisions for large natural disturbances
A key point in the discussions on
provisions for large natural disturbances is what would trigger their
application. Discussions have centred on a trigger set on the basis of total
GHG emissions of parties in 1990 (excl. LULUCF), i.e. as a percentage of 1990
emissions. This makes sense because, in the absence of methods for separating
natural and human induced effects (IPCC, 2010), the cap would relate to and
limit compliance risk. As noted above, events of large disturbances could
result from major grass or forest fires, storms and pest/disease outbreaks.
Reporting on the related effects on emissions is currently scarce, which would
suggest that the application of the provisions may be limited unless monitoring
and reporting is improved. Also, in cases where wood can be salvaged, for
instance following storms, the related emissions would be spread out over a
number of years (up to 35 years) if MSs can account for changes in the HWP
pool. However, wildfire is a common and big source of natural disturbance
emissions and for which reported data are more frequently available. Figure
A2.4 shows that only some six countries in the EU had fire related emissions in
the period 1990-2008 that exceeded one percent of total 1990 GHG emissions, and
only Portugal and Austria had emissions that at some point exceeded five
percent. Figure A2.4 Fire
related emissions (MtCO2eq.) in the period 1990-2008 as % of 1990
GHG emissions Projections of natural disturbances are
difficult (or impossible). However, for the sake of illustration reported data
in 1990-2008 can be used to estimate the impact on accounting. If, during this
period, a trigger of one percent had been used, on average 5.5 MtCO2
per year would have been excluded from accounting at the EU level, see Table
A2.2, whereas a trigger of five percent would have excluded about 1 MtCO2
per year on average. Table A2.2 Fire
emissions that would have been excluded from EU accounting per year as a result
of different trigger values in the period 1990-2008 Trigger values (set as % of a MS's 1990 GHG emissions excl. LULUCF) || Emissions that would have been excluded from accounting per year in the period 1990-2008 (MtCO2eq.) || Average || min || max 1% || 5588 || 276 || 15474 2% || 2992 || 0 || 12459 3% || 1737 || 0 || 11037 4% || 1327 || 0 || 9663 5% || 980 || 0 || 8325
8.2.2.
Caps on credits and debits in forest management
and their implications
Given that there is always a degree of
uncertainty associated with projections, some parties have called for
quantitative limitations ("caps") on credits and debits generated by
forest management. Whilst technical corrections of reference levels and
provisions for big natural disturbances should eliminate most of the risks of
setting "erroneous" reference levels, some uncertainty remains
(projections inherently involve a degree of uncertainty). Caps could be put in
place to ensure that credits are not issued beyond a reasonable level of
expected mitigation potential relative to the projected emissions and removals,
or indeed that debits are not issued beyond a reasonable level of a potential
deterioration in the sink. Such caps could be designed in a number of
ways; similar to the current cap on forest management, developing countries
have called for a cap on credits only, whereas the EU has expressed a
preference for caps on both credits and debits, albeit with a higher degree of
stringency on the credit side than on the debit side.[54] Discussions have centred on
caps set on the basis of total GHG emissions of parties in 1990 (excl. LULUCF),
i.e. as a percentage of 1990 emissions. As in the case of natural disturbances
this makes sense because the cap relates to compliance (risk). Also, base year
emissions are fixed and not subject to changes due to recalculations. A
possible disadvantage of the construction is that countries with low total GHG
emissions and large forest areas are likely to be more affected by caps than
countries with high total emissions. The Durban negotiations in December 2011
settled with a 3.5% credit cap only, applying no cap on debits. Different cap levels have been discussed
previously in the EU, e.g. 4-6% on credits and 8-12% on debits. Table A2.3
shows the impact on accounting of different caps.[55] The calculations are based on
a 10% change in the harvest rate compared to the forest management reference
level which should be regarded as a conservative approach given that the
reference scenario (including reaching the renewable energy targets in the
RES-D) in this impact assessment is expected to lead to an increase in the
harvest rate of about 4%. Table A2.3
Possible debits and credits arising from a 10% change in the harvest rate MS || Possible debits || Possible credits Resulting from a 10% increase in the harvest rate (in % of total MS GHG emissions in 1990) || Resulting from a 10% decrease in the harvest rate (in % of total MS GHG emissions in 1990) Netherlands || 0% || 0% Greece || 0% || -1% Belgium || 0% || 0% United Kingdom || 0% || 0% Luxembourg || 0% || -1% Denmark || 0% || -1% Italy || 0% || 0% Ireland || 1% || -1% Romania || 1% || -1% Bulgaria || 1% || -1% Germany || 1% || -1% Poland || 1% || -1% Slovakia || 1% || -2% Spain || 1% || -1% Hungary || 1% || -1% Lithuania || 1% || -1% Czech Republic || 1% || -1% France || 1% || -2% Austria || 2% || -2% Slovenia || 2% || -2% Portugal || 2% || -2% Estonia || 2% || -2% Latvia || 5% || -5% Sweden || 9% || -10% Finland || 11% || -9% Source:
Calculations based on Böttcher et al. (2011) and JRC (2011b) If the harvest rate is decreased by 10%,
only Latvia, Sweden and Finland would exceed a cap on credits of 3% by some
0.5, 5 and 4 Mt respectively (see Table A2.4). Only Sweden and Finland would
exceed a cap of 5% by some 3.6 and 2.6 Mt respectively. Table A2.4
Possible net removals excluded from accounting at different cap values, arising
from a 10% decrease in the harvest rate || Emissions excluded from accounting per year at different caps on credits (MtCO2) || 3% || 3.5% || 4% || Latvia || 0.5 || 0.4 || 0.3 || Sweden || 5.1 || 4.7 || 4.4 || Finland || 4.0 || 3.6 || 3.3 || EU || 9.6 || 8.8 || 8.0 || Source:
Calculations based on Böttcher et al. (2011) and JRC (2011b) With the Durban decision all increases in
harvest beyond the expected levels in the Forest Management reference level
would be accounted for in full.
8.3.
Annex III – Methodology (models and calibration)
8.3.1.
Description of the models, their linkages and
their use
The core models used to project emissions
and removals from afforestation, deforestation and forest management are G4M
(from IIASA) and EFISCEN (from the European Forest Institute, EFI). The EUFASOM
model is used to project emissions and removals from cropland and grassland.
Table A3.1 and Figure A3.1 below provide the essential features of the main
models involved and an overview of the modelling architecture. This section first
describes the models and then explains how they have been linked to generate
projections. Details can be found in Böttcher et.al. (2011). EFISCEN (European Forest Information
SCENario) is developed and applied by the European Forest Institute (EFI). It is
a large-scale forest scenario model that projects forest resource development
on regional to European scales. EFISCEN describes the state of the forest as an
area distribution over age- and volume-classes based on national forest
inventory data. Transitions of areas during simulations represent different
natural processes influenced by management regimes and changes in forest area.
Management scenarios are specified at two levels in the model. First, a basic
management regime defines the period during which thinnings can take place and
a minimum age for final fellings. These act as constraints for total harvest
levels. Second, the demand for wood is specified for thinnings and final
fellings and EFISCEN may fell the demanded wood volume if available. Harvest
residues from thinning and final fellings can also be extracted. EFISCEN
projects stem wood volume, increment, age-class distributions, removals, forest
area, natural mortality and deadwood for every five year time-step. With the
help of biomass expansion factors, stem wood volume is converted into
whole-tree biomass and subsequently to carbon stocks. EFISCEN is used for
projecting the net emissions due to forest management, afforestation and
reforestation activities. During the last years initial forest inventory have
been updated with new inventory data for a number of countries. The model was
used to project the forest carbon sinks. G4M (Global Forest Model) estimates the
annual above ground wood increment and harvesting costs. By comparing the
income of managed forest (difference of wood price and harvesting costs and
income by storing carbon in forests) with income by alternative land use on the
same place a decision of afforestation or deforestation is made. G4M is
spatially explicit (currently on a 30"x30" resolution). The model can
use external information (like wood prices, prescribed land-use change) from
other models or data bases, which guarantee food security and land for urban
development or account for disturbances. As outputs, G4M produces estimates of
land-use change, carbon sequestration/emissions in forests, impacts of carbon
incentives (e.g., avoided deforestation), and supply of biomass for bio-energy
and timber. The model includes age classes with one year width. Afforestation
and disasters cause an uneven age-class distribution over a forest landscape.
The model performs final cuts so that all age classes have the same area after
one rotation period. During this age class harmonization time the standing
biomass, increment and amount of harvest is fluctuating due to changes in
age-class distribution and afterwards stabilizing. The main forest management
options considered are species selection, variation of thinning and choice of
rotation length. G4M does not model species explicitly but a change of species
can be emulated by adapting net primary productivity (NPP), wood price and
harvesting costs. The rotation length can be individually chosen but the model
can estimate optimal rotation lengths to maximize increment, maximize stocking
biomass or maximize harvestable biomass. The model was used to project the
forest carbon sinks and the carbon sink enhancement potential and costs (forest
management, afforestation and deforestation. EUFASOM simulates detailed land use and
land management adaptations, commodity market and trade equilibrium
adjustments, and environmental consequences in response to political,
technical, societal, and environmental change scenarios related to agriculture,
forestry, and nature conservation. EUFASOM represents the Member States of the
European Union but also includes commodity supply and demand functions for
non-EU regions covering the entire globe. Likely land use impacts are
determined through constrained welfare maximization. The objective function
maximizes the net economic surplus from all agricultural and forestry markets
and includes the impact of policy incentives and disincentives. Technological
opportunities, physical resource endowments, production capacities,
inter-temporal relationships, and political regulations form important
constraints of EUFASOM. Model output consists of optimal land use allocations
and associated management intensities, related environmental impacts, regional
resource usage, commodity supply, equilibrium market prices, and trade volumes
of the agricultural and forest commodities covered in the model. Technological
options include agricultural and forest management alternatives, livestock
production, bioenergy and forest industry processing. The objective function
incorporates all major drivers for these changes, i.e. cost coefficients for
land use and commodity processing alternatives, adjustment costs for major land
use changes, market price changes for commodities and production factors, trade
costs, political incentives and disincentives, and terminal values for standing
forests. EUFASOM was used to project the net emissions from cropland and
grassland for each EU country and to assess the potential and costs for
increasing the agricultural carbon sink. Table A3.1.
Essential features of the core models involved in the projection of LULUCF
emissions and removals and estimates of potential sink enhancement and their
cost. Model || Description G4M || The Global Forest Model (G4M) provides spatially explicit estimates of annual above- and belowground wood increment, development of above- and belowground forest biomass and costs of forestry options such as forest management, afforestation and deforestation by comparing the income of alternative land uses. EFISCEN || The European Forest Information Scenario Model (EFISCEN) is a large-scale model that assesses the supply of wood and biomass from forests and projects forest resource development on regional to European scale, based on forest inventory data. EFISCEN provides projections on basic forest inventory data (stemwood volume, increment, age-structure), as well as carbon in forest biomass and soil. || EUFASOM || EUFASOM simulates detailed land use and land management adaptations, in this project limited to cropland and grazing land management. The model represents the Member States of the European Union in detail. Land use impacts are determined through constrained welfare maximization. Crop yields, water and fertilizer requirements, and environmental impacts are simulated with the Environmental Policy Integrated Climate (EPIC) model. The projections build on macro projections
of GDP and population which are exogenous to the models used and consistent
with PRIMES information (see Figure A3.1 below). They reflect the recent
economic downturn, followed by sustained economic growth resuming after 2010.
The GLOBIOM[56]
model uses these projections to translate them into demand for timber.[57] Bio-energy demand and supply
was projected by the PRIMES biomass model and other timber demand by GLOBIUM
combined with EFISCEN and G4M.[58]
The EU biomass supply model is consistent with the baseline and reference
scenarios of the PRIMES large scale energy model for Europe.[59] It is an economic supply model
that computes the optimal use of resources and investment in secondary and
final transformation, so as to meet a given demand of final biomass energy
products, driven by the rest of sectors as in PRIMES model. The primary supply
of biomass and waste has been linked with resource origin, availability and
concurrent use (land, forestry, municipal or industrial waste etc). The total
primary production levels for each primary commodity are restricted by the
technical potential of the appropriate primary resource. Figure A3.1.
Flowchart of information exchange between models For the baseline and reference scenarios,
the economic land use models (G4M and GLOBIOM) project domestic production and
consumption, net exports and prices of wood products and changes in land use
for EU member states and other world regions. The sector specific information
from the economic models is used by the forest models (EFISCEN and G4M) to
project GHG emissions and removals. This requires a complex interaction between
models for different sectors with different geographical resolutions and
degrees of detail (see Figure) Data exchange from EFISCEN to EU-FASOM includes:
areas and volumes by age class, species, owner for 2005, rotation lengths and
theoretical harvest potentials. EFISCEN deliveres to G4M data on wood densities
as weighted average by country. GLOBIOM receives from G4M data on forest
management parameters, initial NPV of agricultural land and initial wood
prices. After baseline calculations in GLOBIOM to integrate global competition
of world regions for different commodities the model provides country level
total wood demand to the two detailed forestry models G4M and EFISCEN. In
addition, G4M receives from GLOBIOM information on the development of land and
wood prices. Finally, G4M sends data on the areas of afforestation and
deforestation to EFISCEN.
8.3.2.
Description of uncertainties in the modelling
results
The original model results for each MS are
shown in Böttcher et al. (2011). That report also provides a detailed overview
of the original model-based predicted impacts, their influences and the
underlying assumptions and data. The major elements that affect the uncertainty
and sensitivity of the results can be summarized as follows (see Böttcher et
al. (2011) for more details). The modelling approach used is to a large degree
data-driven. Hence, the quality of the results presented depends heavily on the
quality of the datasets that were used. Efforts were made to harmonise data on
forest area, basic national forestry inventory data (area, age-class structure,
growing stock, increment), wood density, biomass expansion factors and wood
removals between models and where possible with estimates and data from member
states in number of bilateral meetings and e-mail exchanges with MSs.
Harmonisation with datasets used by MSs was not always possible in all cases
and differences in datasets used explain differences in reported and projected
emissions and removals. For instance, historical roundwood removals were used
to initialize the forest models and to estimate the future roundwood removals.
A comparison of FAO data on historical wood removals with national statistics
included in the EU submission to UNFCCC showed significant differences.
Sensitivity analysis indicated that projections were rather sensitive to the
assumed harvest rates. Hence, such differences can have substantial impacts on
the projected CO2 emissions or removals. Given these uncertainties
in the national inventories and differences between models and inventories, the
setting of absolute targets (in relation to a past year) such as done for the
ESD runs the risk that targets are not met (while infeasible) or costs are
higher (or lower) than expected. Furthermore, the evolution of the future
forest management sink in MSs depends on several elements. It is uncertain
which role biomass will play in the EU’s energy portfolio. For 2020 MSs have
presented their views and projections in their National Renewable Action Plans.
For 2030 (and also 2020) the demand for 2030 depends to some degree on the
economic viability of second generation type of technologies that would utilize
wood in an efficient way and the share of other renewable and non-renewable
energy sources. Total wood demand depends further on the degree other wood uses
will grow and this demand change might also be negative if the increased wood
demand for bio-energy would result in higher overall wood prices. Improving
resource efficiency through increased recycling rates can also contribute to
limit the growth in demand of wood. The impact on the EU forest carbon stocks
depends on whether the demand growth will be met by internal production or
increased imports. Whether there is an impact on the forest sink or not depends
further on whether increased production is achieved through changes in forest
management or rather through increased afforestation. Given the relatively high
baseline afforestation rates MSs and rather long rotations in new forests
including species with rather moderate growth rates, significant additional
supply from afforestation is negligible. In addition, future roundwood production is
based on projections by GLOBIOM and PRIMES. The projection by these two models
depends on the same macro-economic developments. Furthermore, it was ensured
that GLOBIOM reproduced the same numbers on bio-energy production. However, the
development of the forest sector was not harmonized between the models. This
could lead to discrepancies in the availability of e.g. black liquor and/or
wood waste between GLOBIOM and PRIMES. Further harmonisation between PRIMES and
GLOBIOM could change the projections of CO2 emission or removals by
forests. The GLOBIOM model projected future wood production for the entire EU.
In doing so it was assumed that production of wood for material use in all EU
countries will increase by the same factor. However, the rate is likely to vary
between countries. For some countries “ceilings” on maximum wood removals might
have be built in to constrain the production of wood to reflect environmental,
technical, social and economical constraints that limit wood supply. The models
EFISCEN and G4M have been developed mainly for even-aged forests and
application of the models to situations other than even-aged forests should be
done with great care. For shorter periods, simulation of increment and growing
stock are probably reasonable, but the age-class distribution will be
unreliable and this will influence growth rates and growing stocks at the
longer term. Finally, the impact of growth changes and
large-scale disturbances due to environmental and/or climate change on the
estimated CO2 emissions and removals were not included. Growth may
decrease in Southern Europe due to reduced water availability, whereas growth
in Northern regions may increase much more. This will affect in changes in
future CO2 removals and emissions. Disturbances were also not
included in the analysis, but could have an important impact. The impact of
growth changes and large-scale disturbances on the development of the LULUCF
sector is difficult to model. The model approach does not account for natural
forest disturbance, i.e. losses through fire, storm and insects. Especially in
boreal and Mediterranean forest ecosystems fire plays a major role in the
carbon cycle. By omitting fire losses, both terms might be biased: forest
productivity and net carbon storage. To account for fire implies also a
consideration of likely management-fire feedbacks. Management can both enhance
and suppress the natural fire regime, through anthropogenic ignition, fire
suppression and fire management by timber exploitation and debris abandonment.
This does not allow a simple ex-post calibration or correction for fire losses.
Storm events and insect damage affect the carbon balance in a different way.
The biomass is not lost but timber quality drops and harvest schedule is shifted.
At the regional level however, shifts in the harvest schedule re likely to be
balanced through market mechanisms. More intense harvest in the affected area
is thus compensated by reduced timber extraction in intact regions.
8.3.3.
Description of the calibration method used
To align differences between models and
inventories the original model results were calibrated. The calibration was
carried out by the JRC and is referred to as JRC (2011b) in the main text and
is consistent with the EU's submission of forest management reference levels to
the UNFCCC.[60] In order to ensure consistency between
models’ results and historical data on net GHG emissions reported to the UNFCCC
by countries, the emissions and removals estimated by the models for the entire
time series (up to 2020) were "calibrated"
(i.e. adjusted) using historical data from the country
for the period 2000-2008 (for which we had both data
from the GHG inventories and data projected by the models). To this end, an "offset" was calculated for two components: · biomass: offset calculated as difference between the average of a
country’s emissions and removals from biomass for the period 2000-2008 and the
average of the models’ estimated emissions and removals from biomass for the
period 2000-2008 · non-biomass pools and GHG sources: offset calculated as the sum of
non-biomass pools and GHG sources as reported by the country for the period
2000-2008, and not estimated by models. The calibrated average of the models, which
is used for the setting of the reference level (for the credit calculation), is
obtained by adding the total offset (biomass offset + non-biomass pools and GHG
sources offset) to the models’ average. In other words, models' results were
adjusted to match the average historical data provided by each country for the
period 2000-2008. This ensures consistency between country data and models’
data in terms of: (i) absolute level of emissions and removals from biomass,
i.e. the calibration „reconciles” differences in estimates which may be due to a
large variety of factors, including different input data, different parameters,
different estimation methods (e.g., some country uses a "stock-change
approach", while the models use a "gain-loss approach"); (ii)
coverage of non-biomass pools and GHG sources. The calibration procedure
automatically incorporates into the projections the average rate (for the
period 2000-2008) of the GHG impact of past disturbances, not estimated by the
model (e.g. emissions from fires). The future trend of emissions and removals
up to 2020 as predicted by the underlying models is not affected (but the
absolute level is affected) by this calibration procedure, but only by the
current forest characteristics (e.g. age structure) and the expected future
harvest demand (be it for materials or energy purposes. This way of calibration implies that model
projections are now more consistent with reported emission inventories (2000 to
2008). The uncertainty in the emission inventory itself (around 35%) still
exists and the fact that national emission inventories do not cover all sources
is also not resolved. Adjusted in this way the model results are closer to
reported data (EEA, 2011) and the difference is less than 5% in 2005 for forest
management. The calibrated G4M model estimates the EU's forest management sink
at 378 MtCO2, EFISCEN estimates 387 MtCO2 and the
reported data suggest 394 MtCO2 for 2005. For afforestation and deforestation
uncertainty is also less than 5%.For grazing land management uncertainty is
higher (10%) but not all countries report this. Still, as with any projections there is
uncertainty. This can best be illustrated for forest management, the most
significant activity. The calibrated G4M model projects a sink of -297 MtCO2
in 2020 in the EU. The EFISCEN model projects -378 MtCO2. The
average is -338 MtCO2. For the UNFCCC process 10 MSs also made their
own projections.[61]
Combining these 10 estimates with the model projections for the other countries
(average of G4M and EFISCEN model) gives a FM sink of -253 MtCO2 (the sum of FM
reference levels without HWP). The result of these 3 projections is a mean of
-313 MtCO2 and an uncertainty of +/- 58 MtCO2 (standard deviation).
The uncertainty is a factor ten higher than the reduction of 5.4 MtCO2
that could be expected from setting a reduction target for the EU on the basis
of €5 per tCO2. Given this uncertainty at an EU level the setting of
absolute targets at an EU level runs the risks of not meeting the target or
meeting the target at higher costs than expected certainly when the majority of
the sink enhancement is expected to come from forest management in 2020.
Agreeing on a reduction of 5 MtCO2 could then imply doing nothing
(if the sink is 58 MtCO2 higher than expected) or having to increase
the sink by 63 MtCO2 in 2020 which is not feasible or would require
very high marginal costs (above €150/t CO2), see Böttcher et al.
(2011). The figure below shows the results of the
models for forest management before calibration. Figure A3.2.
Model results for forest management before calibration The figure below shows the results after
calibration to fit data for the period 2000-2008. Figure A3.3.
Model results for forest management after calibration
8.4.
Annex IV – Model results: projected emissions /
removals and abatement costs
8.4.1.
Projected emissions and removals (Reference
scenario)
This annex presents the model predictions
of emissions and removals in the "reference scenario" for the period
leading up to 2020. A description of the assumptions underpinning the scenario
is given by Böttcher et al. (2011), see also Capros et al (2010). The models
and their calibration are explained in Annex III to this report and in Böttcher
et al. (2011).
8.4.1.1.
Models average
Table A4.1. Net
emissions and emissions for the EU-27 in the reference scenario (models
average) (KtCO2/year) Activity || 2005 (reported) || 2010 || 2015 || 2020 || Average 2013-20 Afforestation/reforestation || -40569 || -43267 || -50727 || -60128 || -53559 Deforestation || 25237 || 18056 || 23990 || 26954 || 24617 Forest management || -383469 || -363818 || -347351 || -325996 || -340721 Cropland management || 66089 || 59038 || 58282 || 54609 || 56961 Grazing land management || -30872 || -33081 || -33081 || -33081 || -33081 Total LULUCF || -363584 || -363072 || -348887 || -337641 || -345782 Note: A minus sign
denotes net emissions and plus sign net removals. Source: Böttcher et
al. (2011) and JRC (2011b)
8.4.1.2.
G4M and EUFASOM
Table A4.2. Net
emissions and emissions for the EU-27 in the reference scenario (G4M + EUFASOM)
(KtCO2/year) Activity || 2005 (reported) || 2010 || 2015 || 2020 || Average 2013-20 Afforestation/reforestation || -40569 || -43267 || -50727 || -60128 || -53559 Deforestation || 25237 || 18056 || 23990 || 26954 || 24617 Forest management || -383469 || -343959 || -313983 || -285295 || -305759 Cropland management || 66089 || 59038 || 58282 || 54609 || 56961 Grazing land management || -30872 || -33081 || -33081 || -33081 || -33081 Total LULUCF || -363583 || -343213 || -315518 || -296940 || -310820 Note: A minus sign
denotes net emissions and plus sign net removals. Source: Böttcher et
al. (2011) and JRC (2011b)
8.4.1.3.
EFISCEN and EUFASOM
Table A4.3. Net
emissions and emissions for the EU-27 in the reference scenario (EFISCEN + EUFASOM)
(KtCO2/year) Activity || 2005 (reported) || 2010 || 2015 || 2020 || Average 2013-20 Afforestation/reforestation || -40569 || -43267 || -50727 || -60128 || -53559 Deforestation || 25237 || 18056 || 23990 || 26954 || 24617 Forest management || -383469 || -383677 || -380718 || -366696 || -375682 Cropland management || 66089 || 59038 || 58282 || 54609 || 56961 Grazing land management || -30872 || -33081 || -33081 || -33081 || -33081 Total LULUCF || -363583 || -382931 || -382254 || -378341 || -380743 Note: A minus sign
denotes net emissions and plus sign net removals. Source: Böttcher et
al. (2011) and JRC (2011b)
8.4.1.4.
Accounting results for individual MSs and the
EU-27(models average)
See table on next page
Table A4.4. Credits (KtCO2 per
year) for three accounting options Accounting option || Accounting option (a) Small changes || Accounting option (b) Likely outcome in the UNFCCC negotiations || Accounting option (c) UNFCCC+ || || (I) models || (II) mix || (I) models || (II) mix Austria || -3953 || -1258 || -5083 || -1363 || -5188 Belgium || -671 || -652 || -651 || -726 || -726 Bulgaria || 284 || 2830 || 944 || 3766 || 1881 Cyprus || -1 || -4 || 155 || -4 || 155 Czech Republic || -114 || 1309 || 1310 || 761 || 762 Denmark || -92 || -1041 || -1481 || -1041 || -1481 Estonia || -1326 || -2199 || -2199 || -3171 || -3171 Finland || -4155 || -1564 || -1833 || -1457 || -1726 France || -13763 || -4253 || -4253 || -6834 || -6834 Germany || -10542 || -3687 || -42780 || -4217 || -43310 Greece || -570 || -274 || -415 || -760 || -901 Hungary || 349 || 961 || 961 || 1053 || 1052 Ireland || -2989 || -2576 || -3603 || -2678 || -3705 Italy || -7946 || 7008 || 7008 || -3887 || -3888 Latvia || -2167 || 1051 || -678 || 888 || -841 Lithuania || -274 || 67 || 95 || -232 || -204 Luxembourg || -28 || 35 || -90 || 53 || -72 Malta || 0 || 0 || 49 || 0 || 49 Netherlands || -184 || 10 || -74 || -54 || -139 Poland || -7409 || -7495 || -18867 || -8307 || -19679 Portugal || -4292 || -4969 || -5645 || -4969 || -5645 Romania || -1251 || 2483 || 1487 || 3817 || 2821 Slovakia || -16 || -390 || -390 || -308 || -308 Slovenia || 247 || 2112 || 761 || 2324 || 972 Spain || -6984 || 1457 || 1586 || 684 || 813 Sweden || -7519 || -2833 || -2833 || -3990 || -3990 UK || -3292 || -617 || -9263 || -4307 || -12953 EU27 || -78658 || -14487 || -85781 || -34958 || -106253 Cumulative (2013-2020) || -6292666 || -115892 || -686248 || -279667 || -850023 -315707 -315707 Note: A minus sign
denotes net credits and a plus sign net debits. Credits for forest management
are calculated in two different ways. In both cases the impact of the cap on
credits of 3.5% of base year emissions is included: (1) ( Modeled
emissions and removals in reference scenario) – (modeled emissions and
removales in the baseline scenario) (2) ( Modeled emissions
and removals in reference scenario) – (forest management reference levels in
LULUCF decision CMP.7). Source:
Calculations based on Böttcher et al. (2011) and JRC (2011) Differences between columns (I) and (II)
are mainly due to different assumptions in future harvest rates between model
projections and the country's projections used to set forest management
reference levels. Some MS (e.g., Germany, Poland, Slovenia, Austria, United
Kingdom) used in their forest management reference levels higher future harvest
rates than assumed by the G4M and Efiscen models. Differences between option
(c) and option (b) reflect the impact of cropland and grazing management. The
large difference between option (c) and option (b) for Italy reflects the
potential credits from grassland management, essentially due to the fact that
Italy reported a large increase of the sink in grassland from 1990 to now (more
than 15 MtCO2 increase). While part of this increase is explainable by the
increased area of other wooded lands since 1990 (which Italy reports under
grassland), further checks may reveal the need for some corrections. Abatement
costs and emission reductions per Member State Table A4.5.
Emission reductions and annual costs in 2020 by Member States at various carbon
prices MS || Emissions reduction (MtCO2) at different prices || Annual cost (mln €) at different prices €5/tCO2 || €15/tCO2 || €30/tCO2 || €5/tCO2 || €15/tCO2 || €30/tCO2 Austria || 0.0 || 0.0 || 0.2 || 0.0 || 0.0 || 6.1 Belgium || 0.0 || 0.0 || 0.1 || 0.0 || 0.0 || 4.1 Bulgaria || 1.3 || 1.3 || 2.4 || 0.6 || 0.6 || 4.1 Cyprus || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Czech Rep. || 0.3 || 0.4 || 0.4 || 1.7 || 2.2 || 2.2 Denmark || 0.1 || 0.1 || 0.1 || 0.6 || 0.6 || 4.1 Estonia || 0.7 || 0.7 || 0.9 || 3.7 || 3.7 || 7.3 Finland || 1.3 || 1.9 || 2.3 || 6.7 || 15.7 || 24.2 France || 0.3 || 0.6 || 0.6 || 1.3 || 6.9 || 6.9 Germany || 0.0 || 0.0 || 0.5 || 0.0 || 0.0 || 16.3 Greece || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Hungary || 0.1 || 0.1 || 0.4 || 0.3 || 0.3 || 6.3 Ireland || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Italy || 0.5 || 0.5 || 0.5 || 2.4 || 2.4 || 2.4 Latvia || 0.2 || 0.3 || 0.3 || 1.1 || 2.4 || 2.4 Lithuania || 0.2 || 0.2 || 0.2 || 0.9 || 0.9 || 0.9 Luxembourg || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Malta || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Netherlands || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Poland || 0.0 || 0.0 || 0.2 || 0.0 || 0.4 || 5.9 Portugal || 0.0 || 0.0 || 0.1 || 0.2 || 0.2 || 3.3 Romania || 0.0 || 0.5 || 0.5 || 0.0 || 4.8 || 4.8 Slovakia || 0.2 || 0.2 || 0.3 || 1.0 || 1.0 || 3.7 Slovenia || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 Spain || 0.1 || 0.1 || 0.1 || 0.4 || 0.4 || 0.4 Sweden || 0.0 || 0.0 || 0.4 || 0.0 || 0.2 || 11.4 UK || 0.1 || 0.1 || 0.2 || 0.6 || 0.6 || 4.1 EU27 || 5.4 || 7.0 || 10.7 || 26.4 || 58.2 || 150.9 Note: Costs are in
€2008. Source: Böttcher et
al. (2011).
8.4.2.
Distribution of costs
Table A4.6.
Distribution of (net) abatement costs (in million € in 2008 prices) || Option 2.II || Option 3 Include LULUCF as a separate framework || Include LULUCF in the ESD (Target) Accounting option || (a) || (b) || (c) || (a) || (b) || (c) Austria || 0 || 0 || 0 || -8 || -5 || -5 Belgium || 0 || 0 || 0 || -1 || -3 || -3 Bulgaria || 6 || 6 || 6 || 0 || 12 || 15 Cyprus || 0 || 0 || 0 || 0 || 0 || 0 Czech Rep. || 2 || 2 || 2 || 0 || 6 || 4 Denmark || 0 || 0 || 0 || 0 || -1 || -4 Estonia || 4 || 4 || 4 || -3 || -14 || -17 Finland || 11 || 7 || 7 || -9 || -7 || -6 France || 4 || 1 || 1 || -29 || -24 || -32 Germany || 0 || 0 || 0 || -22 || -16 || -17 Greece || 0 || 0 || 0 || -1 || -1 || -3 Hungary || 0 || 0 || 0 || 1 || 4 || 4 Ireland || 0 || 0 || 0 || -6 || -11 || -11 Italy || 2 || 2 || 2 || -16 || 29 || -17 Latvia || 2 || 1 || 1 || -1 || 11 || 10 Lithuania || 1 || 1 || 1 || -2 || -3 || -4 Luxembourg || 0 || 0 || 0 || 1 || 2 || 2 Malta || 0 || 0 || 0 || 0 || 0 || 0 Netherlands || 0 || 0 || 0 || 0 || 0 || 0 Poland || 0 || 0 || 0 || -15 || -32 || -33 Portugal || 0 || 0 || 0 || -9 || -17 || -20 Romania || 5 || 0 || 0 || -8 || 8 || 7 Slovakia || 1 || 1 || 1 || 0 || -1 || -1 Slovenia || 0 || 0 || 0 || 1 || 9 || 9 Spain || 0 || 0 || 0 || -14 || 19 || 3 Sweden || 0 || 0 || 0 || -16 || -18 || -21 UK || 1 || 1 || 1 || -7 || -3 || -17 EU-27 || 40 || 27 || 27 || -166 || -55 || -156 Source: Calculations
based on an updated version of Böttcher et al. (2011), JRC (2011b) reflecting
the UNFCCC review mid 2011 and results of the PRIMES-GAINS models.
8.5.
Annex V – Discarded options
This annex outlines the policy and accounting options that have not
been considered in detail in this impact assessment and the reasons why.
8.5.1.
Discarded policy options
The option of including LULUCF in the EU ETS has not been considered
in detail in this impact assessment. It has already been the subject of two
studies by the Commission.[62]
Both argue against including LULUCF in the EU ETS for the following reasons
(applicable to the use of LULUCF for project off-sets such as CDM and/or as a
fully included sector): · There are considerable risks related to the temporary and reversible
nature of LULUCF activities in a company-based trading system designed to
handle permanent emissions reductions. No modalities have been developed to
mitigate the impacts of non-permanence, high uncertainties (including the
likelihood of necessary recalculations), jeopardising the environmental
effectiveness of the EU ETS. · Simplicity, transparency and predictability of the EU ETS would be
reduced considerably. · The inclusion of LULUCF in the EU ETS would require land holdings to
be subject to monitoring rules. This would require a quality of monitoring and
reporting at the holding level that is comparable to the monitoring and
reporting of emissions from the installations currently covered by the system.
The currently available guidance for monitoring LULUCF (IPCC, 2003; IPCC, 2006)
has been designed for national inventory systems and are not applicable to
monitoring at the farm/land holding level. This has three major implications: (1)
A whole new monitoring system and protocol would
need to be developed for use on all types of land, a resource and time
demanding process. (2)
As LULUCF monitoring systems and approaches are
scale-dependent, the compatibility/consistency of farm/holding level estimates
with those of national data could not be guaranteed, and therefore would
require a separate system to handle the unavoidable discrepancies. (3)
Because of the much higher resolution of
monitoring, the cost of the system (both monitoring and administrative) would
be orders of magnitude more expensive than that of a national inventory, and
significantly higher than monitoring and transaction costs in the current EU
ETS sectors (see e.g. Annex I for a comparison of uncertainties between
sectors). · Because of the relatively high variability of credits/debits
potentially affecting the system (and quantity in the case of project
off-sets), the functioning of the carbon market might be undermined. These findings have subsequently been
confirmed by an additional review (Watterson et al., 2011) which, based on the
EU ETS legislation, looked at implications of
incorporating LULUCF directly within the scheme. An overview of the key
features of the legislation and related difficulties in integrating LULUCF is
also provided in that report.
8.5.1.1.
Splitting LULUCF according to activities for the
inclusion in different policy frameworks
The mandate requires the Commission to see
if and how the sector can be included in the EU's GHG reduction commitment. It
may be possible to argue that different LULUCF activities could be addressed
under different frameworks just like the energy and industry sectors are
currently divided between the ETS and the ESD. For instance, new guidance on
reporting has been produced by the IPCC (2006) in which non-CO2 GHG
emissions from agriculture and GHG emissions from LULUCF are merged into one
sector. However, this guidance has yet to be adopted by the UNFCCC and the EU
has advocated for continued reporting according to the present sub-sectors. As
no compelling reason for dividing the sector has been found, this option was
not considered further.
8.5.2.
Discarded accounting options
The following accounting options have been
discarded for the reasons given below: · Gross/Net accounting without constraints or with a cap. As in the case of the current KP rules, this accounting approach
does not reflect anthropogenic emissions and removals as it involves accounting
emissions and removals that occur naturally in the commitment period without
comparison to a reference year/period. The accounting results therefore
represent a windfall and limits the incentives for mitigation efforts since the
noise from natural factors is too large for human interventions to be detected.
Such incentives would be further limited with the addition of a cap beyond
which no changes will register in the accounting. · Net/Net accounting for forest management for relative to a single
base year or historical period. As noted above,
emissions and removals in forests depend on a number of natural
(regional/geographical) circumstances such as variations in growing conditions
(temperature, precipitation and droughts) and natural disturbances (storms,
fires) as well as past and present management practices (rotation lengths,
which affect the distribution of age classes in forest stands and therefore the
rate of removals). A single base year or historical period is not able to
reflect these factors and resulting cyclical impacts on emissions and removals
or their inter-annual variations (see Figure 3 in the main text). Instead, such
an approach would create a random impact on accounting. Figure A5.1 illustrates
this point by showing the effects on accounting of forest management for a
fictive commitment period of 1990-2008 (reported data) by changing the base
year from 1990 to 1991. The effects of management practices are limited between
two years, yet the differences in accounting would be as high as 70% in terms
of debits / credits. · Inclusion of wetland or rewetting and drainage on land with organic
soil. The activity relating to wetland management
was defined in the negotiations under the KP in Durban in December 2011, but
the IPCC Guidance (IPCC, 2006) on reporting of Wetlands has not yet been
adopted by Parties to the UNFCCC (earlier IPCC guidance does not fully address
Wetland). In addition, a mandatory inclusion of agricultural and forestry
activities (and their soils) would implicitly cover around 90% of emissions
from organic soils in the EU (JRC, 2011a). The activity should however be
considered for mandatory inclusion in subsequent commitment periods. Figure A5.1.
Illustration: Impact on accounting of changing from 1990 to 1991 as a base year
(using reported data in 1990 to 2008 as a fictive commitment period) Note: Each observation shows the impact on
accounting for a MS. Source: Based on
EEA (2011)
9.
Bibliography
Arrouays D., Morvan X., Saby N.P.A., Richer
de Forges A., Le Bas C., Bellamy P.H., Beréneyi Üveges J., Freudenschuss A.,
Jones A.R., Jones R.J.A., Kibblewhite M.G., Simota C., Verdoodt A. and
Verheijen F.G.A. (eds.) (2008), Environmental assessment of soil for monitoring,
Volume IIa: Inventory and monitoring, EUR 23490 EN/2A, Office for the Official
Publications of the European Communities, Luxemburg, 188pp. Böttcher H., Verkerk H., Gusti M., Havlik
P. and Schneider U. (2011), Analysis of potential and costs of LULUCF use by EU
Member States, Final Report of contract nr 07.0303/2009/541003 (European
Commission, DG Climate Action), IIASA, Laxenburg. (http://www.iiasa.ac.at/Research/FOR/LULUCF/LULUCF_Final_Report_Sep21_2011_UNFCCC_review_update.pdf). Canadell J.P, Kirschbaum M.U.F., Kurz W.A.,
Sanz M.-J., Schlamadinger B. and Yamagata Y. (2007), Factoring out natural and
indirect human effects on terrestrial carbon sources and sinks, Environmental
Science and Policy 10 (2007) 370-384. Capros P., Mantzos L., Tasios N., De Vita
A. and Kouvaritakis N. (2010), EU energy trends to 2030 – UPDATE 2009, European
Commission, Directorate-General for Energy in collaboration with Climate Action
DG and Mobility and Transport DG. Luxembourg: Publications Office of the
European Union, 2010. ISBN 978-92-79-16191-9. Cienciala E., Seufert G., Blujdea V.,
Grassi G. and Exnerová Z. (eds.) (2010), Harmonised methods for assessing
carbon sequestration in European forests, Results of
the Project “Study under EEC 2152/2003 Forest Focus regulation on developing harmonized methods for assessing carbon sequestration in
European forests, EUR 24300 EN, Office for the Official
Publications of the European Union, Luxembourg. EC (2003) Regulation (EC) No 2152/2003 of
the European Parliament and of the Council of 17 November 2003 concerning
monitoring of forests and environmental interactions in the Community (Forest
Focus), Official Journal L 324 , 11/12/2003 P. 001 – 0008. European Environment Agency (2010), Annual
European Union greenhouse gas inventory 1990–2008 and inventory report 2010 –
Submission to the UNFCCC Secretariat, Technical report No /2010. European Environment Agency (2011),
Greenhouse gas data viewer, http://dataservice.eea.europa.eu/PivotApp/pivot.aspx?pivotid=475 Entec (2011a), Public consultation on the
role of agriculture and forestry in achieving the EU's climate change
commitments – Results. Report European Commission. Final report, February 2011. Entec (2011b), Member States' responses to
a consultation on the role of agriculture and forestry in achieving the EU's
climate change commitments – Results. Report European Commission. Final report,
January 2011. Eurostat (2008), Forest based industries in
the EU-27, Statistics in focus 74/2008, Office for the Official Publications of
the European Communities, Luxembourg. Eurostat (2009), Forestry statistics,
Eurostat pocketbooks, Office for the Official Publications of the European
Union, Luxembourg. Fry I. (2002), Twists
and turns in the jungle: Exploring the evolution of land use, land-use change
and forestry decisions within the Kyoto Protocol, RECIEL 11 (2) 2002, ISSN 0962
8797. Fry (2007), More
twists, turns and stumbles in the jungle: A further exploration of land use,
land-use change and forestry decisions within the Kyoto Protocol, RECIEL 16 (3)
2007, ISSN 0962 8797. Hiederer R., Michéli E. and Durrant T.
(2011), Evaluation of BioSoil demonstration project – Soil data analysis. EUR
24729 EN. Publications Office of the European Union. 155pp. Höhne N., Wartmann S., Herold A. and
Freibauer A. (2007), The rules for land use, land use change and forestry under
the Kyoto Protocol – Lessons learned for the future climate negotiations,
Environmental Science and Policy 10 (2007) 535-369. IPCC (2010), Revisiting the use of managed
land as a proxy for estimating national anthropogenic emissions and removals,
eds: Eggleston H.S., Srivastava N., Tanabe K., Baasansuren J., Meeting report
5-7 May, 2009, INPE, Sao Jose dos Campos, Brazil, Pub. IGES, Japan 2010. IPCC (2007), Climate Change 2007: The
Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change Solomon, S.,
D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, 996 pp. IPCC (2006), 2006 IPCC Guidelines for
National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe
K. (eds). Published: IGES, Japan. IPCC (2003), Good practice guidance for
land use, land use change and forestry, Prepared by the
National Greenhouse Gas Inventories Programme, Penman J., Gytarsky M., Hiraishi
T., Krug T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K.
and Wagner F. (eds.). Published: IGES, Japan. IPCC (2000), Good practice guidance and
uncertainty management in national greenhouse gas inventories. IPCC (1996), Revised
1996 IPCC Guidelines for National Greenhouse Gas Inventories. JRC (2011a), Report on the state of play of
monitoring, reporting and verification in the EU, prepared the Joint Research
Centre and included as Annex I to this report.JRC (2011b), Calibration of model
estimates by Böttcher et al. (2011), prepared for this report and the
submission of forest management reference levels to the UNFCCC in summer 2011. Kangas K. and Baudin A. (2003), Modelling
and projections of forest products demand, supply and trade in Europe – A study
prepared for the European Forest Sector Outlook Study (EFSOS), Geneva timber
and forest discussion papers, ECE/TIM/DP/30, Food and Agriculture Organization
of the United Nations. Kibblewhite M.G., Jones R.J.A.,
Montanarella L., Baritz R., Huber S., Arrouays D., Micheli E. and Stephens, M.
(eds) (2008). Environmental Assessment of Soil for Monitoring Volume VI: Soil
Monitoring System for Europe. EUR 23490 EN/6, Office for the Official
Publications of the European Communities Luxembourg, 72pp. Kuikman P., Matthews R., Watterson J., Ward
J., Lesschen J-P., Mackie E., Webb J. and Oenema O. (forthcoming, 2011), Policy
options for including LULUCF in the Community reduction commitment and policy
instruments for increasing GHG mitigation efforts in the LULUCF and agriculture
sectors. Report for the European Commission. Lawrence M., McRoberts R.E, Tomppo E.,
Gschwantner T. and Gabler K. (2010), Comparisons of National Forest
Inventories, in Tomppo E., Gschwantner T., Lawrence M., McRoberts R.E. (eds.)
(2010), National Forest Inventories. Pathways for Common Reporting. 1st edition. Springer, ISBN: 978-90-481-3232-4. Leip, A. (2010), Quantitative quality
assessment of the greenhouse gas inventory for agriculture in Europe, Climatic
Change (2010) 103: 245-261, DOI 10.1007/s10584-010-9915-5. Mantau U. et al. (2010), EUwood – Real
potential for changes in growth and use of EU forests. Final report.
Hamburg/Germany, June 2010. 160pp. Mäkipää R., Häkkinen M., Muukkonen P. and
Peltoniemi M. (2008), The costs of monitoring changes in forest soil carbon
stocks, Boreal Environment Research 13 (suppl. B): 120-30), ISSN 1797-2469,
Helsinki 25 November 2008. Matthews R.W., Robertson K.A., Marland G.
and Marland E. (2007), Carbon in wood products and product substitution. In
Freer-Smith P.H. Broadmeadow M.S.J. and Lynch J.M. (eds.), Forestry and Climate
Change, CAB International: Wallingford, 91-104. McRoberts R., Ståhl G., Vidal C., Lawrence
M., Tomppo E., Schadauer K., Chirici G. and Bastrup-Birk A. (2010) in Tomppo
E., Gschwantner T., Lawrence M., McRoberts R.E. (eds.) (2010), National Forest
Inventories. Pathways for Common Reporting. 1st
edition. Springer, ISBN: 978-90-481-3232-4. Nabuurs G.J., Masera O., Andrasko K.,
Benitez-Ponce P., Boer R., Dutschke M., Elsiddig E., Ford-Robertson J.,
Frumhoff P., Karjalainen T., Krankina O., Kurz W.A., Matsumoto M., Oyhantcabal
W., Ravindranath N.H., Sanz Sanchez M.J. and Zhang X. (2007): Forestry. In
Climate Change 2007: Mitigation. Contribution of Working Group III to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B.
Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA. Rüter S. (2011), Projections of Net‐Emissions
from Harvested Wood Products in European Countries. Work Report No. 2011/x of
the Institute of Wood Technology and Wood Biology, Johann Heinrich von Thünen‐Institute
(vTI). Hamburg, 62 p. Sathre R. and O'Connor J. (2010), A
synthesis of research on wood products and greenhouse gas impacts, 2nd
edition, Vancouver, B. C. FP Innovations, 117p. (Technical Report No. TR-19R). Schlamadinger B., Bird N., Johns T., Brown
S., Canadell J., Ciccarese L., Dutschke M., Fiedler J., Fischlin A, Fearnside
P., Forner C., Freibauer A., Frumhoff P., Hoene N., Kirschbaum M.U.F., Labt A.,
Marland G., Michaelowa A., Montanarella L., Moutinho P., Murdiyarso D., Pena
N., Pingoud K., Rakonczay Z., Rametsteiner E., Rock J., Sanz M.J., Schneider
U.A., Shvidenko A., Skutsch M., Smith P., Somogyi Z., Trines E., Ward M., and
Yamagata Y. (2007a), A synopsis of land use, land use change and forestry
(LULUCF) under the Kyoto Protocol and Marrakesh Accords, Environmental Science
and Policy 10 (2007) 271-282. Schlamadinger B., Johns T., Ciccarese L.,
Braun M., Sato A., Senyaz A., Stephens P., Takahashi M. and Zhang Xiaoquan
(2007b), Options for including land use in a climate agreement post-2012:
improving the Kyoto Protocol approach. Environmental Science and Policy 10
(2007) 295-305. Smith P., Martino D., Cai Z., Gwary D.,
Janzen H., Kumar P., McCarl B., Ogle S., O’Mara F., Rice C., Scholes B.,
Sirotenko O. (2007): Agriculture. In Climate Change 2007: Mitigation.
Contribution of Working Group III to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch,
R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA. Stolbovoy V., Montanarella L., Filippi N.,
Jones A., Gallego J. and Grassi G. (2007), Soil sampling protocol to certify
the changes of organic carbon stock in mineral soil of the Euroopean Union.
Version 2. EUR 21576 EN/2. 56 pp. Office for Official Publications of the
European Communities, Luxembourg. ISBN: 978-9279-05379-5. Tomppo E., Gschwantner T., Lawrence M.,
McRoberts R.E. (eds.) (2010), National Forest Inventories. Pathways for Common
Reporting. 1st edition. Springer, ISBN:
978-90-481-3232-4. UNECE (2011), UNECE
statistical database. Forestry statistics/socioeconomic functions/ number of
forest holdings. http://w3.unece.org/pxweb/dialog/Saveshow.asp?lang=1 [1] Source: IPCC (2003; 2006), with the exception
definitions marked with a star (*). [2] Decision 1/CP.16 [3] European Council, Brussels, 29-30 October 2009,
Presidency conclusions 15265/1/09, as reaffirmed by the Environment Council,
Brussels, 14 March 2011. [4] European Parliament resolution of 4 February 2009 on
"2050: The future begins today – Recommendations for the EU's future
integrated policy on climate change; resolution of 11 March 2009 on an EU
strategy for a comprehensive climate change agreement in Copenhagen and the
adequate provision of financing for climate change policy; resolution of 25
November 2009 on the EU strategy for the Copenhagen Conference on Climate
Change (COP 15) [5] Europe 2020: A strategy for smart, sustainable and
inclusive growth COM(2010) 2020 final, Brussels, 3.3.2010, adopted by the
European Council on 17 June 2010 [6] Article 9 of Decision 406/2009/EC of the European
Parliament and of the Council of 23 April 2009 on the effort of Member States
to reduce their greenhouse gas emissions to meet the Community's greenhouse gas
emission reduction commitments up to 2020 [7] Other greenhouse gases from agricultural activities,
e.g. methane and nitrous oxide from ruminants and fertilisers, do not count
under LULUCF, which deals primarily with carbon emissions and removals in
vegetation and soils. [8] Council Conclusions 17 June 2010, http://ec.europa.eu/eu2020/pdf/council_conclusion_17_june_en.pdf [9] Europe
2020 – A European strategy for smart, sustainable and inclusive growth, http://europa.eu/press_room/pdf/complet_en_barroso___007_-_europe_2020_-_en_version.pdf
[10] European Commission (2010), "The CAP Towards 2020
– Meeting the food, natural resources and territorial challenges of the
future", COM(2010) 672 final. [11] Directive 28/2009/EC of the European Parliament and of
the council of 23 April 2009 on the promotion of the use of energy from
renewable sources. [12] Decision 1/CMP.6 [13] Communication from the Commission to the European
Parliament, the Council, the European Economic and Social Committee and the
Committee of the Regions, Brussels 8.3.2011, COM(2011) 112 final. [14] Decision No 406/2009/EC of the European Parliament and
of the Council of 23 April 2009 on the effort of Member States to reduce their
greenhouse gas emissions to meet the Community's greenhouse gas emission
reduction commitments up to 2020. [15] The key categories are those categories that have a
significant influence on a country’s total inventory of greenhouse gases in
terms of the absolute level or trend of emissions and removals (the uncertainty
of estimates may also be taken into account). Having accurate estimates for the
key categories should be priority for countries during inventory resource
allocation. Methods to estimate key categories are provided by IPCC. [16] A tier represents a level of methodological complexity
to estimate emissions or removals. In this respect, it is useful to recall the
two basic inputs required to estimate emissions or removals: “activity data”
and “emissions factors”. For LULUCF, activity data typically refers to the area
of a category; emission factors refer to emissions/removals per unit area. Any
estimate of emissions or removals can be expresses as activity data multiplied
by the relevant emission factor. Tier 1 is the basic IPCC method (associated to
spatially coarse activity data and default IPCC emission factors), Tier 2 is an
intermediate method (i.e. often using the same method as tier 1, but with
higher resolution activity data are country-specific emission factors), and
Tier 3 is the most demanding method in terms of complexity and data
requirements (i.e. country-specific methods or models that use high-resolution
and finely disaggregated activity data and emissions factors). [17] Compare Tables A1.10 and A1.11 in Annex I where tiers 2
or 3 should be applied to key categories. [18] See Watterson et al. (2011) and e.g. Schlamadinger et al.
(2007b), Höhne et al. (2007) and Fry (2002 and 2007) for an overview. [19] In this context "Business as Usual" assumes
that Member States will reach their 20% reduction targets, including the
targets for renewable energy. [20] The submission is the basis for forest management
accounting and has undergone a review in accordance with Decision 2/CMP.6, see
the UNFCCC website http://unfccc.int/meetings/ad_hoc_working_groups/kp/items/5896.php
for information [21] The choice of activities is based on key categories at
the EU level in UNFCCC reporting. In this report, monitoring and reporting are generally
discussed in terms of the UNFCCC rather than the KP since the former is the
basis for all reporting and an important part of the latter is currently done
on a voluntary basis (which means that data are not reported). The following
subcategories of the LULUCF sector of the EU 15 GHG inventory were key
categories for the trend and the level assessment in 2008 (for CO2): forest
land, cropland, grassland and land converted to settlements. [22] A mitigation strategy aimed at maintaining or
increasing carbon stocks while producing a annual sustained yield of timber,
fibre or energy is expected to generate the largest sustained mitigation
benefit (Nabuurs et al., 2007). However, the correct formulation of such a
strategy requires that emissions from the different uses are accurately reflected
in accounting. [23] See Article 9 of Decision 406/2009/EC. [24] Decision 280//2004/EC of the
European Parliament and of the Council of 11 February 2004 concerning a
mechanism for monitoring Community greenhouse gas emissions and for
implementing the Kyoto Protocol. [25] This view was confirmed also in a different but related
public consultation on the Green Paper on Forest Protection and Information
in the EU: Preparing Forests for Climate Change[25],
where a majority argued for more harmonised and more readily available
information about EU forests, with links being made to a variety of policies,
including climate change mitigation, see http://ec.europa.eu/environment/forests/fprotection.htm. [26] Moving up higher tiers means that the same
methodological approach as tier 1 may be used (for tier 2) but emission and
stock change factors must be based on country- or region-specific data, should
have a higher temporal and spatial resolution and should use more disaggregated
activity data as compared to tier 1 (IPCC, 2006). [27] Decision 2/CMP.6. [28] In particular to determine the extent of the event (in
terms of areas of land affected), estimate the GHG emissions resulting from
disturbance of that land, formally declare the emissions as resulting from the
disturbance, remove these emissions from the calculated GHG balance for the
period, provided evidence can be presented to show that the emissions were
beyond the control of MSs, demonstrate that actions are being taken to
remediate the impacts of the event (e.g. restore carbon stocks on the affected
land), and show that emissions associated with salvage
logging were not excluded. [29] The three studies are: (1) Impact
Assessment of the Directive of the European parliament and of the Council
amending Directive 2003/87/EC so as to improve and extend the EU greenhouse gas
emission allowance trading system. {COM(2008) 16 final}{SEC(2008) 53}, (2) Extended
Impact Assessment on the Directive of the European Parliament and of the
Council amending Directive establishing a scheme for greenhouse gas emission
allowance trading within the Community in respect of the Kyoto Protocol's
project based mechanisms {COM(2003) 403 final}{SEC(2003) 785}, and (3)
Watterson et al. (2011). [30] This carbon price is equal to the expected EU-wide
carbon price of meeting the targets under the effort sharing decision, based on
the analysis underlying Commission Staff Working Document (SEC(2010) 650
accompanying the Communication (2010) 265 final "Analysis of options to
move beyond 20% greenhouse gas emission reductions and assessing the risk of
carbon leakage." [31] See Böttcher et al (2011), p.29 based on PRIMES results
for energy and forest models for non-energy timber demand. [32] The effect on the price of the ESD sectors was based on
a set of dedicated runs of the PRIMES-GAINS models and are driven mainly by the
expected marginal costs for reducing non-CO2 GHG emissions in the ESD sectors. [33] Based on the GLOBIOM model and consistent with UN/FAO
data (Kangas and Baudin, 2003; p. 49-51). [34] Based on the detailed non-GHG cost curves for 2020 from
the GAINS model, see http://ec.europa.eu/clima/documentation/docs/non_co2emissions_may2010_en.pdf
[35] As noted in Box 3, MSs have submitted reference levels
(excluding and including harvested wood products) to the UNFCCC for review in
anticipation of a decision on accounting rules at the world climate summit in
Durban later this year. Some 15 MSs base their harvest projections on Böttcher
et al. (2011), the remaining MSs use national models (see submissions to the
UNFCCC as requested by Decision 2/CMP.6, http://unfccc.int/meetings/ad_hoc_working_groups/kp/items/5896.php). Based on the
harvest projections, the pool of HWP in use was calculated, following the
approach suggested by FCCC/KP/AWG/2010/CRP.4/Rev.4 (p.31, paragraph 7). [36] See http://www.micro-fuel.eu/about-the-project [37] The Land Use/Cover Area frame statistical Survey. The
name reflects the methodology used to collect the information. Estimates of the
area occupied by different land use or land cover types are computed on the
basis of observations taken at more than 250 000 sample points throughout the
EU rather than mapping the entire area under investigation. By repeating the
survey every few years, changes to land use and land management can be
identified (see http://epp.eurostat.ec.europa.eu/portal/page/portal/lucas/introduction
for further information). In 2009, the survey was extended to include soil
sampling on 22 000 sites across the Union. [38] Defined by IPCC (2003) as "…consisting of measurements taken from similar but separate locations
that represent a temporal sequence in land use or management, for example,
years since deforestation. Efforts are made to control all other between-site
differences (e.g., by selecting areas with similar soil type, topography,
previous vegetation). Chronosequences are often used as a surrogate for
experimental studies or measurements repeated over time at the same location."
[39] During the 2009 survey, soil samples were collected
from more than 22 000 LUCAS point locations. [40] CEC (2010) Analysis of options to move beyond 20%
greenhouse emission reductions and assessing the risk of carbon leakage,
background information and analysis. Part II (COM (2010) 265) final. See page
46 and page 55 i.e. [41] Based on the 2010 GHG inventories submitted to UNFCCC. Note
that Cyprus and Malta are not yet Annex-I countries and thus do not submit GHG
inventories to UNFCCC. The information collected here for these countries come
from the GHG reporting under the EU monitoring mechanism. [42] The GHG inventory includes the Common Reporting Format
(CRF) tables, containing the estimates of emissions/removals, and the National
Inventory Report (NIR), containing the description of how the estimates were
obtained along with other information (e.g., on uncertainties, QA/QC,
time-series consistency, recalculations, verification, etc.). [43] The IPCC's GPG and Uncertainty Management in National
Greenhouse Gas Inventories (IPCC GPG, 2000) identifies a key category as “one
that is prioritised within the national inventory system because its estimate
has a significant influence on a country's total inventory of direct GHGs in
terms of the absolute level of emissions, the trend in emissions, or both”. The
approaches to determine key categories in LULUCF are described in Chapter 5.4
of IPCC GPG-LULUCF. In the context of the Kyoto Protocol, each activity under
Articles 3.3 and 3.4 (if elected) is a category. Furthermore, it is good practice
to evaluate possible further disaggregation into subcategories (e.g. forest
land converted to cropland) for purposes of choosing appropriate methods and
prioritizing resources. In this regard, it is good practice to identify any
subcategory as “key” if it accounts for 25-30% of the overall emissions or
removals of the corresponding category. [44] For each C pool, IPCC GPG-LULUCF provides methods
according to 3 Tiers of increasing complexity and certainty in estimates. [45] Under the Convention reporting the C pools to be
reported are: Living Biomass (above- and below-ground), Dead Organic Matter
(DOM, including litter and dead wood) and Soil (including mineral and organic
soils). [46] IPCC GPG-LULUCF assumes no change in C stocks for the
following cases: (i) Tier 1 for DOM and mineral soils in FLrFL, for DOM in LcFL,
and for Biomass in GLrGL; (ii) Tier 1 and 2 for DOM in CLrCL and GLrGL. See
table A1.1 for more details. [47] IPCC GPG-LULUCF assumes no change in C stocks for the
following cases: (i) Tier 1 for DOM and mineral soils in FLrFL, for DOM in
LcFL, and for Biomass in GLrGL; (ii) Tier 1 and 2 for DOM in CLrCL and GLrGL.
See table A1.1 for more details. [48] IPCC GPG for LULUCF, Annex A: Glossary, p. G.2. [49] Both IPCC GPG-LULUCF and UNFCCC guidelines for
reviewing GHG inventories recommend to consider national circumstances when
evaluating the methodological choice (i,e. tier level) in relation to the key
category analysis, This means assessing the resources and capacities needed to
improve the GHG inventory in relation to the Party’s possibilities. [50] The Land Use/Cover Area frame statistical Survey. The
name reflects the methodology used to collect the information. Estimates of the
area occupied by different land use or land cover types are computed on the
basis of observations taken at more than 250,000 sample points throughout the
EU rather than mapping the entire area under investigation. By repeating the
survey every few years, changes to land use and land management can be
identified. [51] The ENVironmental Assessment of Soil for mOnitoring
(ENVASSO) project was funded 2006-08 as Scientific Support to Policy under the
European Commission 6th Framework Programme of Research. [52] Regulation (EC) No 2152/2003 of the European Parliament and
of the Council of 17 November 2003 concerning monitoring of forests and
environmental interactions in the Community, now funded via LIFE+, provides for
measures to e.g. promote harmonised collection, handling and assessment of
data; improve data evaluation at Community level; improve the quality of data
and information gathered; and develop forest monitoring activities. [53] During the 2009 survey, soil samples were collected
from more than 22,000 LUCAS point locations. [54] See the March 2011 Environment Council Conclusions. [55] Based on Böttcher
et al. (2011) and JRC (2011b) who also provide the forest
management reference levels for 15 MSs. [56] GLOBIOM is a global static partial equilibrium model integrating the
agricultural, livestock, bioenergy and forestry sectors with the aim to give
policy advice on global issues concerning land use competition between the
major land-based production sectors. [57] See main assumptions in Capros et al. (2010) . [58] See http://www.e3mlab.ntua.gr/e3mlab/PRIMES%20Manual/THE_NEW_PRIMES_BIOMASS_MODEL.pdf [59] See http://www.e3mlab.ntua.gr/e3mlab/PRIMES%20Manual/The_PRIMES_MODEL_2008.pdf [60] See http://unfccc.int/meetings/ad_hoc_working_groups/kp/items/5896.php
. [61] http://unfccc.int/files/meetings/ad_hoc_working_groups/kp/application/pdf/awgkp_eu_2011_rev.pdf
[62] Impact Assessment of the
Directive of the European parliament and of the Council amending Directive
2003/87/EC so as to improve and extend the EU greenhouse gas emission allowance
trading system. {COM(2008) 16 final}{SEC(2008) 53} and Extended Impact
Assessment on the Directive of the European Parliament and of the Council
amending Directive establishing a scheme for greenhouse gas emission allowance
trading within the Community in respect of the Kyoto Protocol's project based
mechanisms {COM(2003) 403 final}{SEC(2003) 785}